feature hypothesis definition

We are back in Europe and hope you join us!

Prague, Czech Republic, 15 – 17, May 2023

Evolving the Scaled Agile Framework:

Update to SAFe 5

Guidance for organizing around value, DevSecOps, and agility for business teams

• SAFe Contributors
• Extended SAFe Guidance
• Community Contributions
• SAFe Beyond IT
• Books on SAFe
• Download SAFe Posters & Graphics
• Presentations & Videos
• FAQs on how to use SAFe content and trademarks
• What’s new in the SAFe 5.1 Big Picture
• Recommended Reading
• Learn about the Community
• Member Login
• SAFe Implementation Roadmap
• Find a Transformation Partner
• Find a Platform Partner
• Customer Stories
• SAFe Training

There’s innovation in Linux. There are some really good technical features that I’m proud of. There are capabilities in Linux that aren’t in other operating systems. —Linus Torvalds, creator of Linux

Features and Capabilities

Features also lend themselves to the Lean UX process model, which includes a definition of the Minimum Marketable Feature (MMF), a benefit hypothesis, and acceptance criteria. The MMF helps limit the scope and investment, enhances agility, and provides fast feedback.  Capabilities behave the same way as features. However, they are at a higher level of abstraction and support the definition and development of large Solutions .

Features and capabilities are central to the SAFe Requirements Model . They are critical to defining, planning, and implementing  Solution value. Figure 1 provides a broader context for these work items:

Figure 1 shows that solutions are developed using features. Each reflects a service provided by the system that fulfills some important stakeholder needs. They are maintained in the Program Backlog and are sized to fit in a Program Increment (PI) so that each delivers new value. Features can originate from either the local context of the Agile Release Train (ART) or they may result from splitting Epics or capabilities.

The  Program and Solution Kanban  systems support the flow of features and capabilities, where they progress through the funnel, analyzing, backlog, implementing, validating, deployment, and release states. This process provides reasoned economic analysis, technical impact, and strategy for incremental implementation.

Product Management and System Architect/Engineering  own the features and enablers, respectively. Nonfunctional Requirements (NFRs) define system attributes such as security, reliability, performance, maintainability, scalability, and usability. NFRs serve as constraints or restrictions on the design of the system across the different backlogs. Features are prioritized using Weighted Shortest Job First (WSJF)  and are planned and reviewed at PI boundaries. They are split into  Stories , and are implemented, integrated, tested, and demonstrated as the functionality becomes available.

Discovering and Describing Features

Design Thinking takes a Customer-Centric approach to create desirable and sustainable products. Design thinking tools including personas, empathy maps, and customer journey maps provide empathy towards and deeper understanding of customers and users. Together, they provide a rich context to better understand features and their potential benefits.

Features are defined using a Features and Benefits (FAB) Matrix:

• Feature – A short phrase giving a name and context
• Benefit hypothesis – The proposed measurable benefit to the end-user or business

Avoid defining features with the ‘ user story voice ‘ format that’s designed to support one user role; features typically provide functionality for multiple user roles. Furthermore, using the same method to describe user stories and features may confuse the business, especially since they are not normally familiar with stories.

Figure 2 illustrates an example FAB with four different features:

Creating and Managing Features

Product Managers, in collaboration with Product Owners , and other key stakeholders, define features in the local context of an ART. Some arise as a result of splitting epics.

System Architects typically create enabler features. The program backlog is used to maintain enablers alongside business features.  Enablers pave the Architectural Runway and support exploration, or may provide the infrastructure needed to develop, test, and integrate the initiative.

Just like business features, enabler features may originate from epics or emerge locally at the ART level. Enablers that make it through the Kanban system will be subject to capacity allocation in the program backlog to ensure that enough emphasis is placed on both furthering the solution and extending the architectural runway.  At each PI boundary, the percentage of resources to be allocated to new features (or capabilities) versus enablers is estimated to guide the train.

Prioritizing Features

The WSJF prioritization model is used to sequence jobs (e.g. features, capabilities) based on the economics of product development flow. Since implementing the right jobs in the right sequence produces the maximum economic benefit—it is hard to overstate the importance of this critical process.

Product and Solution Management have the authority to prioritize features, while System and Solution Architects and Engineering have the authority to prioritize enabler features.

Estimating Features

Feature estimation supports forecasting value delivery, applying WSJF prioritization, and sizing epics by splitting them into features and summing their individual estimates. Feature estimation usually occurs in the analysis state of the  Program Kanban and relies on normalized estimation techniques, similar to the methods used by Agile teams (see the Iteration Planning article for more detail).  During analysis, select subject matter experts from the ART engage in exploration activities and preliminary sizing. During the analysis state, sizing features do not require splitting them into stories or the inclusion of all the teams that might develop them.

Accepting Features

Acceptance criteria are used to determine whether the implementation is correct and delivers the business benefits. Figure 3 provides an example:

Acceptance criteria mitigate implementation risk and enable early validation of the benefit hypothesis by creating alignment between product management, stakeholders, and developers. Acceptance criteria can also be used as the source of stories. As with stories, acceptance criteria are often transformed into acceptance tests with Behavior-Driven Development (BDD).

Product Management is responsible for accepting the features. They use acceptance criteria to determine whether the functionality is properly implemented and nonfunctional requirements met.

Capabilities

Most of this article is devoted to describing the definition and implementation of features, as they are the most common description of system behavior. Capabilities exhibit the same characteristics and practices as features. For example, they:

• Are described using a phrase and benefit hypothesis
• Are sized to fit within a PI, however, they often take multiple ARTs to implement
• Are reasoned about and approved using the Solution Kanban .  The Solution Backlog holds approved capabilities
• Have associated enablers to describe and bring visibility to all the technical work necessary to support efficient development and delivery of business capabilities
• Are accepted by Solution Managers , who use the acceptance criteria to determine whether the functionality is fit for purpose

Capabilities may originate in the local context of the solution or occur as a result of splitting portfolio epics that may cut across more than one Value Stream . Another potential source of capabilities is the Solution Context , where some aspect of the environment may require new solution functionality.

Splitting Features and Capabilities

Capabilities must be decomposed into features to be implemented. They, in turn, are split into stories consumable by teams within an iteration. SAFe provides ten patterns for splitting work, as described in Leffingwell [1], chapter 6.

• Workflow steps
• Business rule variations
• Major effort
• Simple/complex
• Variations in data
• Data methods
• Deferring system qualities
• Use-case scenarios
• Breaking out a spike

Figure 4 illustrates splitting a capability into features.

[1] Leffingwell, Dean. Agile Software Requirements: Lean Requirements Practices for Teams, Programs, and the Enterprise . Addison-Wesley, 2011 .

Last update: 10 February 2021

Privacy Overview

Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.

Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.

Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.

Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.

The ART of SAFe

Applying Lean and Agile techniques at scale to bring about effective, sustainable improvement in Culture, Execution and Business Results

Monday, January 8, 2018

Effective feature templates for safe, introduction, how much detail is needed, and by when.

• Prior to WSJF assessment
• Prior to PI Planning

Feature Canvas

New Product: “The current state of the [domain] has focussed primarily on [customer segments, pain points, etc]. What existing products/services fail to address is [this gap] Our product/service will address this gap by [vision/strategy] Our initial focus will be [this segment]”

Existing Product: “Our [service/product] is intended to achieve [these goals]. We have observed that the [product/service] isn’t meeting [these goals] which is causing [this adverse effect] to our business. How might we improve [service/product] so that our customers are more successful based on [these measurable criteria]?”

“We believe this [business outcome] will be achieved if [these users] successfully achieve [this user outcome] with [this feature]”.

Sample Completed Canvas

A glimpse at how you might visualise your next WSJF estimation workshop

Detail beyond the Canvas

• User Journeys:  Some framing UX exploration is often very useful in preparing a Feature, and makes a great support to teams during PI planning.  
• Architectural Impact Assessment: Some form of deliberate architectural consideration of the potential impact of the feature is critical in most complex environments.  It should rarely be more than a page – I find a common approach is one to two paragraphs of text accompanied by a high level sequence diagram identifying expected interactions between architectural layers.
• Change Management Impacts: How do we get from deployed software to realised value?  Who will need training?  Are Work Instructions required?  

Tuning your Template

Who completes the canvas/template, 30 comments:.

Awesome work Mark! We have created some for clients too that we can't share. :-(

Thanks for sharing Mark - these are really useful. I really like the hypothesis statements for features and think that this is a major enhancement in SAFE 4.5. I wrote a blog post about it here: http://runningmann.co.za/2017/09/25/the-power-of-feature-hypotheses/ that you might be interested in.

These are awesome Mark. Thanks for sharing

Thanks for sharing your experience on this area with the community Mark. Feature Templates are a very common requirement for Agile practitioners, maybe you can persuade the SAFe community to include an artefact like this in the framework.

Information was good,i like your post. Looking forward for more on this topic. product management

This is great! Do you have the template format available so we don't have to replicate?

great stuff, how would you differentiate this from SAFe Epics

Other problems include editing resistance, lack of or significantly delayed communications and lack of professionalism. website

I discovered your blog site site on the search engines and check several of your early posts. Always maintain up the very good operate. I recently additional increase Rss to my MSN News Reader. Looking for toward reading much more on your part later on!… apple tablet mockup

I genuinely treasure your work , Great post. ipad mockups

When I originally commented I clicked the -Notify me when new surveys are added- checkbox and from now on every time a comment is added I buy four emails with similar comment. Perhaps there is any way you are able to eliminate me from that service? Thanks! web design company san francisco

Good post and a nice summation of the problem. My only problem with the analysis is given that much of the population joined the chorus of deregulatory mythology, given vested interest is inclined toward perpetuation of the current system and given a lack of a popular cheerleader for your arguments, I’m not seeing much in the way of change. web design company san francisco

Cutting up pictures, reestablishing of old photos, working around with watermarks, and other progressed tips and deceives can likewise be found in a high caliber and significant Adobe photograph shop instructional exercise. Professional graphic design

Very efficiently written information. It will be beneficial to anybody who utilizes it, including me. Keep up the good work. For sure i will check out more posts. This site seems to get a good amount of visitors. essay writing service

Their willingness to partner with us has been great. UI design agency

Hey enormous stuff or pleasant information you are offering here. best branding agencies

Nice blog Mark How can I get a downloadable version of this Canvas?

I think you can make video about it. If you want to promote your channel on youtube you can buy youtube subscribers for it

Thanks for this incredible information. I think you could make a video about feauture templates for sale and post it on Instagram. By the way if you want to get more followers for your profile, you can repeatly use the help of https://viplikes.net/buy-instagram-followers to quickly boost their number.

Socialize Club- Buy Facebook live stream views cheap in price but high in quality.We provide you with 100% Real Facebook live stream views delivered at your facebook live video instantly.

Packaging should function as a barrier to air, water vapor, dust, and other contaminants. Because food items might be in liquid form, they must be leak-proof to avoid loss during transportation. BOPP film supplier

percetakan buku online di jakarta percetakan murah jakarta percetakan online jakarta percetakan Jakarta timur cetak murah jakarta cetak online jakarta digital printing jakarta print murah jakarta jasa print murah cetak buku murah di jakarta timur

I use the aforementioned off-page SEO techniques and am currently seeing the results of my labors with page 1 Google search engine rankings. For example, Deanna Hibbs, a full-time working mother, discovered internet marketing while searching for a work schedule other than 9 to 5. SEO philadelphia

thank you for share an informative blog article with us. kitimesblog

Essential phases

This comment has been removed by the author.

Produk Elektronik Terbaik Saat Ini Bahan dapur memasak ikan Bumbu dapur ayam goreng Menjual bahan dapur Shin Tae Yong coah terbaik sepanjang masa Justin hubner bek andalan timnas indonesia Nathan Tjoe a on bek bule berdarah indonesia yang membela timnas Jualan tanpa modal hanya dengan perlengkapan dapur

Preparing Features for PI Planning - Part Three

A lot of work goes into the continuous refinement of a product’s backlog. This is true as much for Feature Backlogs as it is for Story backlogs. When using SAF e® if it often seems that too much work goes into preparing the Features and getting them ready for PI Planning. This series of blogs focusses on various topics concerned with preparing Features for PI Planning:

• Just say no to waterfall thinking
• The seven deadly sins of Feature Preparation
• What does it mean to be Feature ready? (this article)
• Some practical tips to avoid waterfalling Features

Download the Feature State Cards that go with this article below

So what does it mean for a Feature to be “Ready”? And just as importantly when do they need to be “Ready”?

As many people have observed ( Read Mike Cohn’s article ) Definition of Ready can be a dangerous thing leading to waterfall behavior and strict hand-overs between Product Owners and their teams. In many cases teams would be better off not having one as it often stops them from working on the most important Feature because they believe it’s someone else’s job to get it ready. But keeping this in mind, what would make a suitable definition of ready for a Feature?

Personally, we’ve always been fond of Roman Pichler’s simple definition that to be ready a backlog item should be clear, feasible and testable. This applies as well to Features as it does to Stories, and provides the first ready test for any Product Manager to apply to their Features. If they don’t feel it is clear, feasible or testable they shouldn’t be letting it anywhere near the top of their backlog and into their next PI .

For Features it is worthwhile taking a look at what each of these means.

• Clear – It is clear what the feature means and how it will benefit the customers, users and other stakeholders. Basically, the Product Management / Ownership community can readily explain what the Feature means and explain its intricacies to the development team. If they don’t have the knowledge to explain the Feature and answer the development team’s questions then they are not ready, and by implication neither is the Feature.
• Feasible – it is technically feasible to implement the Feature. This implies that the estimate is accurate enough for prioritization purposes (so the Feature doesn’t have a false position in the backlog), and is small enough to be completed within a Program Increment ( PI ). If there is too much uncertainty or technical risk related to the Feature then an enabler Feature should be broken out to directly address the issues. If the it is not easily understood how the Feature will implemented then the Feature is not ready.
• Testable – it is understood how the Feature will be tested. Not just how its Storied will be tested but also how the Feature as a whole will be tested. In particular it is useful to know whether or not any new or unique types of testing are required. If the Feature is not testable then it is not doable, and therefore not ready.

It is also critically important that any Features selected conform to Bill Wake’s incredibly popular INVEST acronym, which describes the properties of a well-formed backlog items. An acronym that applies as much, if not more, to Features as it does to Stories.

Again, it is worth taking a look at how thinking INVEST can help us to prepare our Features properly.

• Independent – all Features should be thought of as independent from all other Features, particularly when prioritizing and preparing them prior to a PI planning meeting (i.e. getting them Ready). There may be a natural order to the Features in that Feature 2 doesn’t make a lot of sense if Feature 1 isn’t in place. This doesn’t imply dependency as we could implement Feature 2 before Feature 1 if we wanted to, but that doing them in this order wouldn’t make any business sense.

Sometimes Features will require the same changes to be made to the codebase or even contain the same Stories. This doesn’t make the Features dependent on each other but can 1) make them more challenging to implement simultaneously and 2) lead to changes to their estimates as the overlapping Features are implemented. This potential overlap is one of the reasons we like to do collaborative PI planning with the whole team. By simultaneously planning the work on a set of Features the teams can discover and collaborate to exploit any overlaps helping the team-of-teams to deliver even more Features.

• Negotiable – This one is the big one as not only is the priority of the Feature negotiable so are its extent (the number of stories it involves) and its acceptance criteria. PI planning is not just about planning but also negotiating the scope and extent of the Features being planned.
• Valuable – it should go without saying that all Features should have clear value to the business and benefit for the users and other stakeholders.
• Estimable – If the Feature can’t be estimated then it can’t be prioritized. You cannot prioritize a backlog on value alone. Note: the estimates will change over time as Features can overlap (see Independent above) and need to be estimated against the current state and capabilities of the system – an estimate which may well change every PI .
• Sized Appropriately / Small – All Features should be sized appropriately for their position in the Backlog. If the Feature is under consideration for implementation in the next PI then it needs to be small enough to readily fit in the PI . If it isn’t it will need to be sliced. If the Feature is not going to be implemented until a subsequent time-box then it can be any size it likes. If you’re never going to do it then who cares how big it is
• Testable – see the discussion above.

Taking all of this into account, and focusing on what it means for a Feature to be ready to be used as input into PI planning we have put together this simple checklist.

This checklist, as shown in Figure 1, is also available as one of a set of 6 mini-checklist cards that together define the lifecycle of a Feature.

Figure 1: Checklist cards for Feature Evolution

If things go to plan future blogs will further explore the lifecycle of a Feature and the other checklists, but the full set of cards are available for download now below, along with an editable copy of the Ready checklist. Complete the form below to receive the one page PDF of the Feature cards.

In the final blog in this series we will provide some practical advice on activities you can do to get your Features ready for PI planning without falling prey to the seven sins of feature preparation and back into waterfall behavior. Find it here: Some practical tips to avoid waterfalling Features

More Great Agile Content from Ivar Jacobson International:

• How Many Features do you need for PI Planning
• WSJF & Feature Slicing
• All about the PI Planning Management Review & Problem Solving
• Secrets of Dispersed PI Planning
• Estimating in Story Points

Upcoming Training

How to Define Features in Agile Methodology?

Home Blog Agile How to Define Features in Agile Methodology?

Agile projects are known for their simple, iterative approach to cutting through the complexity. Even the most ambitious of Agile projects is taken one step at a time and break down complex work packages and tasks into low-level subtasks. Features and capabilities that are needed in the finished product are listed out and then broken down into manageable chunks, which are taken up and completed, one at a time.

In this article, we will talk about Features in an Agile methodology, what are the characteristics of features and how they are applied. The best Agile certifications will help you get experiential learning.

What is a Feature in Agile Methodology?

A feature is a service or function of the product that delivers business value and fulfils the customer’s need. Each feature is broken down into several user stories, as it is usually too big to be worked on directly. 

A user story is an informal, short description of a part of a software feature that is written from the user’s perspective and talks about how this particular bit of the feature will offer something of value.

In project management software, "Task Prioritization" is a feature that has helped our team to assign task priority levels. Earlier, our team was working together on multiple features altogether. But with agile features, we were able to develop and deliver features independently to our users without waiting for the entire project management system to be completed. Agile methodology features enable teams to respond to changing requirements and provide value regularly.

What are Features Called in Different Agile Methodologies? 

In various Agile methodologies, the term "features" may be referred to by different names, but the essence remains consistent.

• In Scrum, features are often articulated as "Product Backlog Items" or simply "Backlog Items." These represent the functional components, or user stories prioritized for development.  
• In Kanban, similar work units might be termed "Tasks" or "Cards," emphasizing the visual representation on Kanban boards. 
• In Extreme Programming (XP), features are known as "User Stories," encapsulating specific end-user functionalities.
• Furthermore, some methodologies adopt broader terms, such as "Requirements," to encompass features and other project needs. 

The variation in nomenclature reflects the flexibility and adaptability of Agile methodologies to diverse project environments. Despite the terminology nuances, the underlying principle in all Agile frameworks is to deliver incremental value to end-users through iterative development cycles, and the entities representing these increments are fundamental to the Agile process, regardless of the specific terminology used.

What are the Characteristics of Features in Agile Methodology?

To be effective, a feature should always

• User-Centric Focus - Features in Agile are typically defined from the user's perspective, ensuring that the development team prioritizes functionalities that provide direct value to end-users.
• Backlog Management - Features are often represented in a product backlog, a prioritized list of functionalities that guides the development team's work. Prioritization is based on business value and user needs.
• Incremental Delivery - Features are delivered incrementally in small, manageable iterations. This allows for faster releases, continuous feedback, and the ability to adapt to changing requirements.
• Cross-Functional Collaboration - Developing features often involves collaboration among cross-functional teams, where individuals with various skills work together to deliver complete and valuable functionalities.
• User Stories - Features are broken down into user stories, which are small, specific units of work. User stories articulate the functionality from the end-user's perspective and serve as the building blocks for development.
• Acceptance Criteria - Each feature or user story is accompanied by acceptance criteria, which define the conditions that must be met for the feature to be considered complete and ready for release.
• Continuous Refinement - Features are subject to continuous refinement based on feedback from stakeholders, users, and team retrospectives. This iterative process ensures that the product evolves to meet changing needs.
• Adaptability to Change - Agile features are developed with the understanding that requirements may change. The Agile approach encourages embracing changes, even late in the development process, to enhance the product's value.
• Minimal Viable Product (MVP) - Agile often focuses on delivering a Minimal Viable Product, which includes the essential features needed for the product to be functional and valuable. Additional features are then added in subsequent iterations.
• Regular Demonstrations - Completed features are regularly demonstrated to stakeholders and users, fostering transparency and collaboration. This ensures that the delivered functionality aligns with expectations.
• Inspection and Adaptation -  Agile features undergo continuous inspection and adaptation. This involves reviewing progress, obtaining feedback, and adapting plans and features accordingly to enhance the overall product.
• Test-Driven Development (TDD) - In Agile, features are often developed using Test-Driven Development, where tests are created before the code. This ensures that the features meet specified requirements and are free of defects.
• Offer measurable business value,  
• Contain enough information to allow for estimation of the work involved,
• Be small enough to be completed within a program increment or maximum of three months,  
• Be testable by the scrum team and the product management team.

How to Write Features in Agile Methodology? [Step-by-Step]

Writing Agile features requires a user-focused, iterative approach. Here's a step-by-step guide on how to write Agile features:

Step 1: Formulate Benefit Hypothesis

Specify how the functionality will provide advantages to users.

For example  - As a Product Manager, I noticed a problem: many customers prefer calling to rent a service instead of using our website. They do this because they want someone to help them choose the right service. This is causing issues for our team and limiting the number of customers we can reach. That's why I want our system to be able to recommend the best service for customers based on their needs.

This change will make things easier for customers and our team. It's like having a helpful guide built into our system. This information is crucial for the team to understand why we're making this feature and how it will improve things for everyone.

Step 2: Evaluate Business Value

Determine the business value through various methods, considering user volume, feature usage frequency, release timing, and development effort.

I did a quick study to see how helpful it could be. For a week, we asked people who booked a service over the phone if they'd rather do it on the website with personalized advice on services. Surprisingly, 68% said yes because it saves time, and 32% feel better talking to someone to learn about services. I also checked with the marketing team to see how long people spend reading about services and if they book afterward. Turns out, if we add this new feature, we could sell 25% more of the basic services online in a month. It would also save 10% of the support team's time, giving them room for other important tasks. I shared all this info in the feature report because it's vital to decide which features to work on first—those that give us the most benefits without too much effort.

Step 3: Description

Articulate the feature's context and user interactions, emphasizing the necessity rather than the technical implementation.

To describe new feature, I've listed the questions that people often ask our Support team when they book a basic service. These are things they should be able to find on our website. I've also suggested a plan for how users will move through the feature, but we'll talk more about it with the team. The description has important stuff from my view as the Product Manager and techie details that the team, especially the scrum team, will figure out to make the feature work smoothly.

4. Establish Acceptance Criteria/DoD  

Clearly define the conditions that signify the completion of a feature. Similar to acceptance criteria for user stories, features serve definition of done as key criteria and checkpoints for gauging progress in product development.

In my case, I checked if a user submitting for a specific service on the website can retrieve the correct results, ensuring personalization they are looking for. Here, the acceptance criteria do not relate to how features are being implemented but depends on functionality.

Best Practices for Defining Agile Features

According to me, following best practices will leave less room for mistakes. Taking care of the following best practices while using features, will bring more fruitful results.

• Prioritize a user-cantered approach by focusing on the needs, preferences, and challenges of end-users.
• Collaborate with stakeholders to prioritize features based on their value to users and overall business goals.
• Embrace the iterative nature of Agile development, regularly reviewing and adapting features based on feedback, changing market conditions, and evolving project requirements for continuous improvement.
• Foster collaboration among cross-functional teams, including developers, testers, and product owners, to ensure a shared understanding of features for smoother implementation and reduced miscommunication.
• Clearly define acceptance criteria for each feature to establish measurable benchmarks for completion, enhancing transparency and reducing ambiguity.
• Utilize prototyping and mock-ups to visualize feature implementations, clarify requirements, validate design decisions, and ensure a common understanding among team members and stakeholders.

Who Writes Features in Agile Scrum?

The Scrum Guide, considered to be the Bible for all things Scrum, does not lay out any guidelines for the use of features.

However, Scaled Agile, Inc. indicates that the Product Manager is the owner of the Features, which is to say, he or she finally decides what goes into the feature and what is its priority on the Backlog. The features are not necessarily written by the Product Manager, however, and this could be done by others on the team. Going for the professional Scrum Master certification training will help you crack PSM exam on the first go. 

There are several steps in the definition and writing of features. 

• Define the WHY, or the benefit hypothesis - What is the functionality that the users gain from the feature? What are the benefits to be gained from implementing this feature? 
• Calculate the business value - Keep in mind the number of users, how often each of them uses the feature, what is the timeframe within which the feature must be released for it to be useful, and how much effort goes into developing this feature. All these together will help to determine the ROI of the feature and ultimately whether it is worth the effort and cost. Features that bring in the most benefit at least cost will be prioritized. 
• Describe the feature - What is the context and how will it be used? What is the need for the feature? Try to include technical details and any information that is important from the Product Manager’s point of view. 
• Write down the acceptance criteria -  What are the conditions under which the feature can be deemed to be done? This will help to reduce any ambiguity and mark work progress.

Become a project management expert with our online PMP certification courses . Start your journey to success now!

How Big Should the Product Features be?

While there is no hard and fast rule on this, and it is left largely to the convenience of project teams, it is generally agreed that it should be possible to complete a feature within a maximum of three months. 

When using SAFe, a feature is released in one single program increment. 

Teams that are working with investor funding and are getting the funds at regular cycles should be able to showcase a completed feature during each investment cycle, in order to demonstrate that they are progressing on track. 

What are Feature Points?

Feature points represent the amount of work complexity, effort taken, and knowledge required to complete one feature. They are the same as story points, but in the context of a feature rather than a user story.

• Task Complexity Gauge - Feature points help teams gauge how complex a task is, doing planning and managing their work easier.
• Comparing Task Sizes - They work by comparing the size of one task to another, allowing teams to prioritize and allocate resources based on the perceived complexity.
• Essential for Planning - Feature points are crucial for planning in Agile, helping teams decide which tasks to tackle first and ensuring efficient use of time and effort.
• Tracking Work Speed - They contribute to measuring a team's speed in completing tasks (velocity) and predicting how much work can be done in future sprints.
• Learning and Improving -  Feature points support a continuous learning process, allowing teams to adapt and improve their estimation accuracy over time as they gain more insights into the project's intricacies.

Features vs. Epics in Agile

In Agile development, features and epics are terms used to describe work units, but they differ in scope and complexity. Here's a breakdown of the distinctions between features and epics.

Feature Breakdown Structure (FBS)

The Feature Breakdown Structure (FBS) is a project management tool used to decompose and organize features into user stories within a product or project. Like a Work Breakdown Structure (WBS), which breaks down project tasks, the FBS focuses specifically on features. It provides a hierarchical representation of features, breaking them into smaller, more manageable components.

At the top level, the FBS outlines the significant features essential for the project's success. Each feature is further detailed as you descend through the hierarchy, often into sub-features or user stories. This breakdown aids in a more comprehensive understanding of the project scope, allowing for effective planning, prioritization, and resource allocation.

The FBS is a visual and organizational tool fostering clear communication among team members, stakeholders, and product owners. It helps identify dependencies, estimate efforts, and ensure that the project team aligns with the project objectives. The FBS is particularly valuable in Agile environments, facilitating iterative development and adaptability as the project progresses, ensuring that the delivered features meet user needs and align with project goals.

Why Use Features in Agile Scrum and Not Only User Stories?

A feature is something that is sizeable enough to deliver measurable value to customers and creates a large chunk of functionality. Features are used to describe the functionality at a macro level, and they are required to create schedules and plan the high-level release of the product.

Scrum works on the premise of short development cycles called Sprints, which usually last between 2 weeks and a month but not longer. One feature is typically completed over several sprints. In one sprint, only several user stories can be completed and not, perhaps, an entire feature.

• Holistic Functionality View -  Features encapsulate a broader set of functionalities, providing a holistic perspective. This helps teams understand how user stories contribute to overall product goals.
• Improved Planning and Prioritization -  Features assist in high-level planning by grouping related user stories. This enables effective release prioritization, aligning with the product roadmap and aiding strategic decision-making.
• Enhanced Communication and Collaboration -  Features are a communication tool, bridging gaps between technical and non-technical stakeholders. They establish a common language for discussions, fostering better collaboration and understanding among team members and stakeholders.
• Flexibility in Development -  Features offer flexibility for adapting to changing requirements. Teams can adjust focus within a feature without disrupting the project's overall structure.
• Easier Tracking and Reporting -  Features simplify progress tracking and reporting. Project managers and stakeholders can monitor feature status for a higher-level project overview, facilitating communication with those not involved in day-to-day development.
• Facilitation of Release Planning -  Features are crucial in release planning, enabling teams to structure releases based on completed functionalities. This ensures a smoother release process and effective management of stakeholder expectations.

Building an Initial Feature List

At the very start, before the release planning and iteration planning can happen, the team must sit together and list out as many potential features for the system as possible at this stage. Feature requests can come from many sources, and one person should be allocated to collate all these requests. While this could be the product manager, it could also be a customer proxy, a business analyst or someone who is responsible and accountable to the team. 

The team should refine these requirements, weeding out duplicate items, features that are not possible to implement, and requests that are very vague. As the features are identified, they are added to the list so that they can become a part of the planning processes. 

This initial feature list can be considered to be a preliminary outline that can be used as input to chart out the release and first iteration. It is not required to wait until all features are defined before getting started on the actual work, and it is also understood that the original list, descriptions, and priorities will evolve over time. 

Instead of waiting for everything to get detailed out at the outset, the team can get to work with the initial list without wasting any valuable time. As new features which could be critical get identified, they are simply added into the evolving release plan and will get delivered during a subsequent iteration. As the project progresses, the work adapts itself to accommodate new priorities, additional information from stakeholders, and the changing industry dynamics.

Advantages of Breaking Down Features into Smaller User Stories

User stories, as we have learned, represent smaller chunks of work while features represent fully formed functionalities of the product. There are many advantages to breaking down the features into functionalities, and the main ones are these:

• Stories narrow down the focus: Stories are small, doable portions of the work that do not overwhelm the developer. They represent an entire piece of functionality, however small it is, and so can measure incremental progress.
• Stories fit into a sprint: Features are too large to be completed within a sprint, but stories can be finished within this duration. This allows more efficient scheduling and planning of sprint tasks.
• Stories capture both intent and outcome: A product manager (who is not required to be technically fluent) can easily describe the outcome of a story to the developer, so that he or she can understand the intent.
• Stories mitigate the risk: As big stories come with a lot more complexity, they also involve more risk. When features are broken down into smaller stories, this risk is mitigated. Anny erroneous assumptions can be curtailed within a few days rather than several weeks into development. 

Feature vs Task Planning

Features come into play at a macro level of planning, and it is essential that at a later point they will need to be broken down into tasks and estimated. This is done during sprint planning and release planning.

Feature planning and estimates help to schedule releases and iterations. Task planning and estimates help to allocate resources and plan the tasks within an iteration.

Since the nature of agile project plans is always fluid and not very precise, feature estimates need not exactly map to a number of task estimates, but there should be a rough approximation between the two.

Delve into the most popular Agile Category Courses

In summary, features in agile methodology represents specific user functionalities, contributing to incremental development. They are more granular than epics, addressing immediate user needs. 

On the other hand, epics are larger entities aligning with broader business goals. Recognizing the difference is crucial for effective planning. Features enable detailed, user-focused development, ensuring responsiveness to evolving user requirements within the Agile framework.

Frequently Asked Questions (FAQs)

Features are tracked in Agile through visual tools like Kanban boards or project management software. Progress is monitored by tracking the completion status of individual user stories within features. Regular team meetings and sprint reviews further ensure effective monitoring.

Estimating features in Agile involves assigning story points or relative sizing. Teams collectively evaluate the complexity and effort required for each feature during sprint planning, fostering a shared understanding and aiding in prioritization.

In Agile, features are higher-level functionalities that encompass multiple user stories. Features provide a broader perspective, while user stories break down specific functionalities within features, offering more granular details.

Lindy Quick

Lindy Quick, SPCT, is a dynamic Transformation Architect and Senior Business Agility Consultant with a proven track record of success in driving agile transformations. With expertise in multiple agile frameworks, including SAFe, Scrum, and Kanban, Lindy has led impactful transformations across diverse industries such as manufacturing, defense, insurance/financial, and federal government. Lindy's exceptional communication, leadership, and problem-solving skills have earned her a reputation as a trusted advisor. Currently associated with KnowledgeHut and upGrad, Lindy fosters Lean-Agile principles and mindset through coaching, training, and successful execution of transformations. With a passion for effective value delivery, Lindy is a sought-after expert in the field.

Avail your free 1:1 mentorship session.

Something went wrong

Upcoming Agile Management Batches & Dates

Try for free

Minimum Requirements for a Feature

Minimum requirements for a feature: what is a benefit hypothesis, minimum requirements for a feature: who writes acceptance criteria, feature definition.

A feature, within the scaled Agile definition (SAfe), requires a benefits hypothesis and acceptance criteria. These establish what and why you are testing and how you will determine success or failure. Each feature will usually have three key components that form the minimum requirements: Beneficiaries. These are needed upfront to establish the hypothesis and the acceptance criteria. Benefit hypothesis.  Acceptance criteria.

.css-uphcpb{position:absolute;left:0;top:-87px;} How do you write a feature in agile?

The minimum requirements already discussed can be made more detailed by covering a few related areas:

The beneficiaries.

The benefit hypothesis. 

The feature’s business value.

A clear feature description.

Acceptance criteria.

The two fundamental elements of the benefits hypothesis and the acceptance criteria can be unpacked in a little more detail to illustrate their individual and collective roles for features.

The benefit hypothesis is the business value that the feature is expected to deliver. Similar to a scientific hypothesis, this is a statement that will ultimately be tested to see if it is correct. A good formula to use is:

If (proposition), then (benefit)

The proposition is what your team plans to deliver, while the benefit is the value that this will deliver. Benefits can be business-side and include:

Increased efficiency.

Greater transparency.

Cost reductions.

Improved data streams.

Increased revenue.

On the client-side, benefits can include:

Increase customer satisfaction.

Improved functionality.

Greater simplicity for better customer experiences (CX).

How likely is this proposition able to deliver this benefit? 

Is this feature’s success rate quantifiable? 

You must be able to validate your hypothesis to measure the relative success or lack of success of the related feature. Ongoing optimization or even a decision to pivot will not be possible without the ability to quantify how well the proposition succeeded in delivering the benefit.

In Scaled Agile Frameworks (SAFe) a feature’s acceptance criteria are usually written by the stakeholder or the product owner. The acceptance criteria should provide a framework to measure whether the benefit is being delivered by the proposition. In other words, has the feature shown the benefit hypothesis to be correct? If not, is it possible to optimize or would it be better to pivot?

The main functions of acceptance criteria are:

Determine if the feature has been implemented correctly.

Establish whether the business benefits are being delivered.

Mitigate implementation risks.

Facilitate early validation testing to prevent unnecessary costs and effort.

Inform user stories and functional tests.

General FAQ

Glossary categories

Feedback Management

Prioritization

Product Management

Product Strategy

Roadmapping

Prioritize with confidence

Book a demo

Experience the new way of doing product management

• Games & Quizzes
• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center

• When did science begin?
• Where was science invented?

scientific hypothesis

Our editors will review what you’ve submitted and determine whether to revise the article.

• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

The Running Mann

Humorous Anecdotes, Observations & Accounts of Marathon Running & Agile Adventures.

The Power of Feature Hypotheses

One of the improvements SAFe version 4.5 introduced was incorporating practices from “ The Lean Startup ” into the framework – specifically the use of benefit hypothesis statements into features and epics. This is a story of how well this worked for us.

We were worried about the standard of feature writing across our teams and also wanted to bring them up to speed with the SAFe 4.5 feature hypothesis thinking. Therefore, we organised a Friday afternoon “Lunch and Learn” session for interested team members.

The main objective was to go over a practical example from one of our feature teams and convert an existing feature into a ‘ valuable feature hypothesis statement ‘.

[If you are unfamiliar with the Lean Startup concept of hypotheses, please see supporting post:  Using Hypothesis Statements for Features in Software Development ]

Getting A Feature Benefit Hypothesis Statement

We started with the feature captured below that, no disrespect to team, was not very well written. The definition I like to use for a well written feature is that: ‘ someone outside the team can read it once and understand what needs to be done ’. At this stage I don’t even think that most of the team understood the feature.

Luckily we had the product owner (PO) in the room. We asked him a few probing questions that went something like this.

Team: Why are we doing this change? What is the benefit? PO: We need to update the audit report fields for our customers. Team: Why do they need the additional fields. PO: So that customers can check for errors before they submit them to us for processing. Team: Why do they check for errors? How does this work? PO: All customers have a QA person or someone similar checking these reports to prevent errors being processed. When errors get missed and processed, it wastes time and causes a lot of frustration. Team: So what exactly is the current problem? PO: Today, customer auditors can’t see all the relevant information on the audit reports for cross-checking and referencing so many errors are still processed. Team: What is the intended result of this change? PO: We’ll reduce the error rate by 95%. [And BAM! We get our hypothesis]

Interesting takeout: The PO did not come right out with the “95% error reduction” even though it was in his head from the start – it required a conversation to get his knowledge shared with the rest of the team. This is part of the magic of conversation within collocated teams – and the importance of having a PO who works with the team.

The benefit hypothesis makes the “why” clear to without having to write a long document. Everyone in the room quickly came to the same understanding as to what needed to be done and why. A well worded hypothesis statement helps remove ambiguities and focuses the team on what really needs to be done. [To understand why “why?” is important – see Simon Sinek’s TED Talk below.]

The other (perhaps even more) positive outcome is that it gives “purpose” to the teams’ work. I asked the group, “Would you rather (a) update some fields on an audit report or (b) reduce error rates by 95%?” – unsurprisingly there was unanimous agreement that (b) was the more exciting option.

“Reducing error rates by 95%” gives meaning to the team’s work. I am unlikely to go home and proudly tell my kids I updated some fields on an audit report. But telling them that I helped reduce foreign exchange error rates by 95% makes my job as a developer on a transactional banking system sound sexy and important!

Feature Acceptance Criteria & Slicing

We then moved onto the feature acceptance criteria (AC). The simple way I view feature AC is, “ How will we UAT the feature to know that it’s complete and fit for purpose? ” (as opposed to user story AC which are the actual unit test cases).

If you are not able to write clear AC you don’t know enough to proceed with the feature so this was a good test of the strength of our feature hypothesis. Once again it was an interesting and valuable discussion with participants throwing various questions at the PO. A summary is below:

Which countries are in scope?

The PO initially said “all countries”. Further conversation sliced it down to two countries (South Africa and Uganda) where there was definite current need – from there it made sense to split the feature into one for each country. South Africa had the most urgent need so we focused on that and the lower priority Uganda feature went onto the backlog (where it will remain until it becomes priority). The requirement that the initial feature for South Africa be scalable for other countries was included as a non-functional requirement.

Attempted slipping in of a production defect

The PO tried to slip a production defect into the AC (the format of the onscreen and printed reports are different). The team deftly managed to slice the defect off the feature (the defect fix is roughly the same size as the eventual feature). The valid defect was added to the team backlog (where the PO can prioritise it against other work to determine when it will get fixed).

Attempted scope creep

The PO also tried to slip a new requirement into the AC – the ability to be able to save audit reports as a .pdf file. Once again the team deftly convinced the PO that this was not part of the minimum viable product (MVP) by referring to the hypothesis statement (i.e. we don’t need to save to .pdf to test the hypothesis). The valid requirement was also added to the team backlog (and the PO gets to decide when it’s priority enough to be built).

Interesting takeout: In my opinion, the PO was doing his job perfectly – trying to get as much as possible into the feature to maximise the customer benefit. Because he’s part of the overall team having the conversation he gets to understand the trade-offs involved with different decisions. When posed with the question, “Do you want to have the feature done in 2 weeks if we just do MVP or do you want to wait for 2 months if we do ‘ all this other stuff ‘?” a good PO will always go for “ small and fast “.

By reducing the feature to a single (highest priority) country and omitting other requirements that (whilst important) had no impact on the hypothesis statement, the feature ended up being at least 10 times smaller than if we’d tried to include everything. Slicing the feature down to get the most value with the least amount of work drastically increases the speed with which it can be built and dramatically reduces risk.

The team left the room knowing exactly what needed to be done. More importantly everyone had agreed on what didn’t need to be done “right now” (i.e. in the next sprint) because they were not MVP (however these requirements have been added to the team backlog and can be done when the PO prioritises them). The PO was also part of all the decisions taken so he should get no nasty surprises when the feature is presented back as “done”.

Conclusion: Super-Quick Delivery

The best part of this story was that two weeks later when I asked the team “How’s the feature going?”, the reply was “It’s already done.”

The team was able to fully design, build and test a valuable feature in less than two weeks (fitting easily into a 2-week sprint). In the past a feature like this would usually take 12 months to deliver (see the supporting post “ 6 Reasons: From 12 Months to 2 Weeks for Feature Delivery “). By slicing the feature down to its true MVP and the team knowing exactly what was needed and why, the feature flew through the development value stream.

Of course, now the real test is to measure whether our hypothesis proves true and we successfully reduce the error rate by 95% – let’s hope so! We’ll know pretty soon…

The actual reduction in client error rates was 80%. As it stands this has been deemed sufficient. There are no plans to build additional features to further reduce the error rates (as there have been higher priority features to work on). Likewise, the production defect has not been fixed (and may never be) since it has never been a priority compared to other more beneficial work.

I delivered a presentation on “The Power of Feature Hypotheses” at Agile Africa 2018 using this and other examples. Here is the video link from the conference:  https://youtu.be/CUVX1AiqQak?list=PLp6xQ3fl72zIu8FJjDtBUFUS0w1FPQhPZ

I am also more than happy to submit a speaker proposal for your conference. Feel free to contact me via email: [email protected] or Twitter @runningmann100 /  @StuartDMann .

4 Replies to “The Power of Feature Hypotheses”

• Pingback: 6 Reasons: From 12 Months to 2 Weeks for Feature Delivery - The Running Mann
• Pingback: Using Hypothesis Statements for Features in Software Development - The Running Mann

Good account of what we discussed in the session and what was learnt. Going forward it will definitely provide a mechanism for us to access information on the sessions to further unpack.

• Pingback: How George Costanza, Frogger & a Craving For Sushi Help Explain Features & User Stories - The Running Mann

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Please enable JavaScript to submit this form.

Save my name, email, and website in this browser for the next time I comment.

Scrum and Hypothesis Driven Development

• Website for Dave West
• Twitter for Dave West
• LinkedIn for Dave West

Scrum was built to better manage risk and deliver value by focusing on inspection and encouraging adaptation. It uses an empirical approach combined with self organizing, empowered teams to effectively work on complex problems. And after reading Jeff Gothelf ’s and Josh Seiden ’s book “ Sense and Respond: How Successful Organizations Listen to Customers and Create New Products Continuously ”, I realized that the world is full of complex problems. This got me thinking about the relationship between Scrum and modern organizations as they pivot toward becoming able to ‘sense and respond’. So, I decided to ask Jeff Gothelf… Here is a condensed version of our conversation.

Sense & Respond was exactly this attempt to change the hearts and minds of managers, executives and aspiring managers. It makes the case that first and foremost, any business of scale or that seeks to scale is in the software business. We share a series of compelling case studies to illustrate how this is true across nearly every industry. We then move on to the second half of the book where we discuss how managing a software-based business is different. We cover culture, process, staffing, planning, budgeting and incentives. Change has to be holistic.

What you are describing is the challenge of ownership. Product Owner (PO) is the role in the Scrum Framework empowered to make decisions about what and when things are in the product. But disempowerment is a real problem in most organizations, with their POs not having the power to make decisions. Is this something you see when introducing the ideas of Sense and Respond?

There will always be situations where things simply have to get built. Legal and compliance are two great examples of this. In these, low risk, low uncertainty situations a more straightforward execution is usually warranted. That said, just because a feature has to be included for compliance reasons doesn’t mean there is only one way to implement it. What teams will often find is that there is actual flexibility in how these (actual) requirements can be implemented with some being more successful and less distracting to the overall user experience than others. The level of discovery that you would expend on these features is admittedly smaller but it shouldn’t be thrown out altogether as these features still need to figure into a holistic workflow.   

What did you think about this post?

Share with your network.

• Share this page via email
• Share this page on Facebook
• Share this page on Twitter
• Share this page on LinkedIn

View the discussion thread.

Why hypothesis-driven development is key to DevOps

Opensource.com

The definition of DevOps, offered by  Donovan Brown is  "The union of people , process , and products to enable continuous delivery of value to our customers. " It accentuates the importance of continuous delivery of value. Let's discuss how experimentation is at the heart of modern development practices.

Reflecting on the past

Before we get into hypothesis-driven development, let's quickly review how we deliver value using waterfall, agile, deployment rings, and feature flags.

In the days of waterfall , we had predictable and process-driven delivery. However, we only delivered value towards the end of the development lifecycle, often failing late as the solution drifted from the original requirements, or our killer features were outdated by the time we finally shipped.

Here, we have one release X and eight features, which are all deployed and exposed to the patiently waiting user. We are continuously delivering value—but with a typical release cadence of six months to two years, the value of the features declines as the world continues to move on . It worked well enough when there was time to plan and a lower expectation to react to more immediate needs.

The introduction of agile allowed us to create and respond to change so we could continuously deliver working software, sense, learn, and respond.

Now, we have three releases: X.1, X.2, and X.3. After the X.1 release, we improved feature 3 based on feedback and re-deployed it in release X.3. This is a simple example of delivering features more often, focused on working software, and responding to user feedback. We are on the path of continuous delivery, focused on our key stakeholders: our users.

Using deployment rings and/or feature flags , we can decouple release deployment and feature exposure, down to the individual user, to control the exposure—the blast radius—of features. We can conduct experiments; progressively expose, test, enable, and hide features; fine-tune releases, and continuously pivot on learnings and feedback.

When we add feature flags to the previous workflow, we can toggle features to be ON (enabled and exposed) or OFF (hidden).

Here, feature flags for features 2, 4, and 8 are OFF, which results in the user being exposed to fewer of the features. All features have been deployed but are not exposed (yet). We can fine-tune the features (value) of each release after deploying to production.

Ring-based deployment limits the impact (blast) on users while we gradually deploy and evaluate one or more features through observation. Rings allow us to deploy features progressively and have multiple releases (v1, v1.1, and v1.2) running in parallel.

Exposing features in the canary and early-adopter rings enables us to evaluate features without the risk of an all-or-nothing big-bang deployment.

Feature flags decouple release deployment and feature exposure. You "flip the flag" to expose a new feature, perform an emergency rollback by resetting the flag, use rules to hide features, and allow users to toggle preview features.

When you combine deployment rings and feature flags, you can progressively deploy a release through rings and use feature flags to fine-tune the deployed release.

See deploying new releases: Feature flags or rings , what's the cost of feature flags , and breaking down walls between people, process, and products for discussions on feature flags, deployment rings, and related topics.

Adding hypothesis-driven development to the mix

Hypothesis-driven development is based on a series of experiments to validate or disprove a hypothesis in a complex problem domain where we have unknown-unknowns. We want to find viable ideas or fail fast. Instead of developing a monolithic solution and performing a big-bang release, we iterate through hypotheses, evaluating how features perform and, most importantly, how and if customers use them.

Template: We believe {customer/business segment} wants {product/feature/service} because {value proposition}. Example: We believe that users want to be able to select different themes because it will result in improved user satisfaction. We expect 50% or more users to select a non-default theme and to see a 5% increase in user engagement.

Every experiment must be based on a hypothesis, have a measurable conclusion, and contribute to feature and overall product learning. For each experiment, consider these steps:

• Observe your user
• Define a hypothesis and an experiment to assess the hypothesis
• Define clear success criteria (e.g., a 5% increase in user engagement)
• Run the experiment
• Evaluate the results and either accept or reject the hypothesis

Let's have another look at our sample release with eight hypothetical features.

When we deploy each feature, we can observe user behavior and feedback, and prove or disprove the hypothesis that motivated the deployment. As you can see, the experiment fails for features 2 and 6, allowing us to fail-fast and remove them from the solution. We do not want to carry waste that is not delivering value or delighting our users! The experiment for feature 3 is inconclusive, so we adapt the feature, repeat the experiment, and perform A/B testing in Release X.2. Based on observations, we identify the variant feature 3.2 as the winner and re-deploy in release X.3. We only expose the features that passed the experiment and satisfy the users.

Hypothesis-driven development lights up progressive exposure

When we combine hypothesis-driven development with progressive exposure strategies, we can vertically slice our solution, incrementally delivering on our long-term vision. With each slice, we progressively expose experiments, enable features that delight our users and hide those that did not make the cut.

But there is more. When we embrace hypothesis-driven development, we can learn how technology works together, or not, and what our customers need and want. We also complement the test-driven development (TDD) principle. TDD encourages us to write the test first (hypothesis), then confirm our features are correct (experiment), and succeed or fail the test (evaluate). It is all about quality and delighting our users , as outlined in principles 1, 3, and 7  of the Agile Manifesto :

• Our highest priority is to satisfy the customers through early and continuous delivery of value.
• Deliver software often, from a couple of weeks to a couple of months, with a preference to the shorter timescale.
• Working software is the primary measure of progress.

More importantly, we introduce a new mindset that breaks down the walls between development, business, and operations to view, design, develop, deliver, and observe our solution in an iterative series of experiments, adopting features based on scientific analysis, user behavior, and feedback in production. We can evolve our solutions in thin slices through observation and learning in production, a luxury that other engineering disciplines, such as aerospace or civil engineering, can only dream of.

The good news is that hypothesis-driven development supports the empirical process theory and its three pillars: Transparency , Inspection , and Adaption .

But there is more. Based on lean principles, we must pivot or persevere after we measure and inspect the feedback. Using feature toggles in conjunction with hypothesis-driven development, we get the best of both worlds, as well as the ability to use A|B testing to make decisions on feedback, such as likes/dislikes and value/waste.

Hypothesis-driven development:

• Is about a series of experiments to confirm or disprove a hypothesis. Identify value!
• Delivers a measurable conclusion and enables continued learning.
• Enables continuous feedback from the key stakeholder—the user—to understand the unknown-unknowns!
• Enables us to understand the evolving landscape into which we progressively expose value.

Progressive exposure:

• Is not an excuse to hide non-production-ready code. Always ship quality!
• Is about deploying a release of features through rings in production. Limit blast radius!
• Is about enabling or disabling features in production. Fine-tune release values!
• Relies on circuit breakers to protect the infrastructure from implications of progressive exposure. Observe, sense, act!

What have you learned about progressive exposure strategies and hypothesis-driven development? We look forward to your candid feedback.

Comments are closed.

Related content.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism. Run a free check.

Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved July 5, 2024, from https://www.scribbr.com/methodology/hypothesis/

Is this article helpful?

Shona McCombes

Other students also liked, construct validity | definition, types, & examples, what is a conceptual framework | tips & examples, operationalization | a guide with examples, pros & cons, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Trackbacks/Pingbacks

• What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example,…

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

Researcher.Life is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Researcher.Life All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 21+ years of experience in academia, Researcher.Life All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $17 a month !    

Related Posts

What is a Case Study in Research? Definition, Methods, and Examples

How to Write an Academic Biography

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• Privacy Policy

Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Research Techniques – Methods, Types and Examples

Evaluating Research – Process, Examples and...

Dissertation – Format, Example and Template

Scope of the Research – Writing Guide and...

Research Process – Steps, Examples and Tips

Research Objectives – Types, Examples and...

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)