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Statistics, essay questions for Exam 3

Top 99+ Trending Statistics Research Topics for Students

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them.

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

Table of Contents

Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time.

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

Literacy rate in a city.
Abortion and pregnancy rate in the USA.
Eating disorders in the citizens.
Parent role in self-esteem and confidence of the student.
Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

Statistical analysis for legs and head injuries in Football.
Statistical analysis for shoulder and knee injuries in MotoGP.
Deep statistical evaluation for the doping test in sports from the past decade.
Statistical observation on the performance of athletes in the last Olympics.
Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
Statistical evolution to find out the suicide reason among students and adults.
Statistics analysis to find out the effect of divorce on children in a country.
Psychology affects women because of the gender gap in specific country areas.
Statistics analysis to find out the cause of online bullying in students’ lives.
In Psychology, PTSD and descriptive tendencies are discussed.
The function of researchers in statistical testing and probability.
Acceptable significance and probability thresholds in clinical Psychology.
The utilization of hypothesis and the role of P 0.05 for improved comprehension.
What types of statistical data are typically rejected in psychology?
The application of basic statistical principles and reasoning in psychological analysis.
The role of correlation is when several psychological concepts are at risk.
Actual case study learning and modeling are used to generate statistical reports.
In psychology, naturalistic observation is used as a research sample.
How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

Does education have a deep impact on the financial success of an individual?
The investment in digital technology is having a meaningful return for corporations?
The gap of financial wealth between rich and poor in the USA.
A statistical approach to identify the effects of high-frequency trading in financial markets.
Statistics analysis to determine the impact of the multi-agent model in financial markets.

Personalized Medicine Statistics Research Topics

Statistical analysis on the effect of methamphetamine on substance abusers.
Deep research on the impact of the Corona vaccine on the Omnicrone variant.
Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
Statistics analysis to identify the role of genes in the child’s overall immunity.
What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

Generic vs private education is one of the best for the students and has better financial return.
Psychology vs physiology: which leads the person not to quit their addictions?
Effect of breastmilk vs packed milk on the infant child overall development
Which causes more accidents: male alcoholics vs female alcoholics.
What causes the student not to reveal the cyberbullying in front of their parents in most cases.

Easy Statistics Research Topics

Application of statistics in the world of data science
Statistics for finance: how statistics is helping the company to grow their finance
Advantages and disadvantages of Radar chart
Minor marriages in south-east Asia and African countries.
Discussion of ANOVA and correlation.
What statistical methods are most effective for active sports?
When measuring the correctness of college tests, a ranking statistical approach is used.
Statistics play an important role in Data Mining operations.
The practical application of heat estimation in engineering fields.
In the field of speech recognition, statistical analysis is used.
Estimating probiotics: how much time is necessary for an accurate statistical sample?
How will the United States population grow in the next twenty years?
The legislation and statistical reports deal with contentious issues.
The application of empirical entropy approaches with online grammar checking.
Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

Uses of statistics in chemometrics
Statistics in business analytics and business intelligence
Importance of statistics in physics.
Deep discussion about multivariate statistics
Uses of Statistics in machine learning

Survey Topics for Statistics

Gather the data of the most qualified professionals in a specific area.
Survey the time wasted by the students in watching Tvs or Netflix.
Have a survey the fully vaccinated people in the USA
Gather information on the effect of a government survey on the life of citizens
Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

Have a deep decision of Bayes theorems
Discuss the Bayesian hierarchical models
Analysis of the process of Japanese restaurants.
Deep analysis of Lévy’s continuity theorem
Analysis of the principle of maximum entropy

AP Statistics Topics

Discuss about the importance of econometrics
Analyze the pros and cons of Probit Model
Types of probability models and their uses
Deep discussion of ortho stochastic matrix
Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics

National income and the regulation of cryptocurrency.
The benefits and drawbacks of regression analysis.
How can estimate methods be used to correct statistical differences?
Mathematical prediction models vs observation tactics.
In sociology research, there is bias in quantitative data analysis.
Inferential analytical approaches vs. descriptive statistics.
How reliable are AI-based methods in statistical analysis?
The internet news reporting and the fluctuations: statistics reports.
The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

Role of statistics in business in 2023
Importance of business statistics and analytics
What is the role of central tendency and dispersion in statistics
Best process of sampling business data.
Importance of statistics in big data.
The characteristics of business data sampling: benefits and cons of software solutions.
How may two different business tasks be tackled concurrently using linear regression analysis?
In economic data relations, index numbers, random probability, and correctness are all important.
The advantages of a dataset approach to statistics in programming statistics.
Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

Evaluate the role of John Tukey’s contribution to statistics.
The role of statistics to improve ADHD treatment.
The uses and timeline of probability in statistics.
Deep analysis of Gertrude Cox’s experimental design in statistics.
Discuss about Florence Nightingale in statistics.
What sorts of music do college students prefer?
The Main Effect of Different Subjects on Student Performance.
The Importance of Analytics in Statistics Research.
The Influence of a Better Student in Class.
Do extracurricular activities help in the transformation of personalities?
Backbenchers’ Impact on Class Performance.
Medication’s Importance in Class Performance.
Are e-books better than traditional books?
Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further.

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

Introduction of a problem
Methodology explanation and choice.
Statistical research itself is in the main part (Body Part).
Samples deviations and variables.
Lastly, statistical interpretation is your last part (conclusion).

Note: Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer. But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific.

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project.

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects.

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic.

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more.

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

How to Find the Best Online Statistics Homework Help

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Why SPSS Homework Help Is An Important aspect for Students?

The Writing Center • University of North Carolina at Chapel Hill

There are lies, damned lies, and statistics. —Mark Twain

What this handout is about

The purpose of this handout is to help you use statistics to make your argument as effectively as possible.

Introduction

Numbers are power. Apparently freed of all the squishiness and ambiguity of words, numbers and statistics are powerful pieces of evidence that can effectively strengthen any argument. But statistics are not a panacea. As simple and straightforward as these little numbers promise to be, statistics, if not used carefully, can create more problems than they solve.

Many writers lack a firm grasp of the statistics they are using. The average reader does not know how to properly evaluate and interpret the statistics they read. The main reason behind the poor use of statistics is a lack of understanding about what statistics can and cannot do. Many people think that statistics can speak for themselves. But numbers are as ambiguous as words and need just as much explanation.

In many ways, this problem is quite similar to that experienced with direct quotes. Too often, quotes are expected to do all the work and are treated as part of the argument, rather than a piece of evidence requiring interpretation (see our handout on how to quote .) But if you leave the interpretation up to the reader, who knows what sort of off-the-wall interpretations may result? The only way to avoid this danger is to supply the interpretation yourself.

But before we start writing statistics, let’s actually read a few.

Reading statistics

As stated before, numbers are powerful. This is one of the reasons why statistics can be such persuasive pieces of evidence. However, this same power can also make numbers and statistics intimidating. That is, we too often accept them as gospel, without ever questioning their veracity or appropriateness. While this may seem like a positive trait when you plug them into your paper and pray for your reader to submit to their power, remember that before we are writers of statistics, we are readers. And to be effective readers means asking the hard questions. Below you will find a useful set of hard questions to ask of the numbers you find.

1. Does your evidence come from reliable sources?

This is an important question not only with statistics, but with any evidence you use in your papers. As we will see in this handout, there are many ways statistics can be played with and misrepresented in order to produce a desired outcome. Therefore, you want to take your statistics from reliable sources (for more information on finding reliable sources, please see our handout on evaluating print sources ). This is not to say that reliable sources are infallible, but only that they are probably less likely to use deceptive practices. With a credible source, you may not need to worry as much about the questions that follow. Still, remember that reading statistics is a bit like being in the middle of a war: trust no one; suspect everyone.

2. What is the data’s background?

Data and statistics do not just fall from heaven fully formed. They are always the product of research. Therefore, to understand the statistics, you should also know where they come from. For example, if the statistics come from a survey or poll, some questions to ask include:

Who asked the questions in the survey/poll?
What, exactly, were the questions?
Who interpreted the data?
What issue prompted the survey/poll?
What (policy/procedure) potentially hinges on the results of the poll?
Who stands to gain from particular interpretations of the data?

All these questions help you orient yourself toward possible biases or weaknesses in the data you are reading. The goal of this exercise is not to find “pure, objective” data but to make any biases explicit, in order to more accurately interpret the evidence.

3. Are all data reported?

In most cases, the answer to this question is easy: no, they aren’t. Therefore, a better way to think about this issue is to ask whether all data have been presented in context. But it is much more complicated when you consider the bigger issue, which is whether the text or source presents enough evidence for you to draw your own conclusion. A reliable source should not exclude data that contradicts or weakens the information presented.

An example can be found on the evening news. If you think about ice storms, which make life so difficult in the winter, you will certainly remember the newscasters warning people to stay off the roads because they are so treacherous. To verify this point, they tell you that the Highway Patrol has already reported 25 accidents during the day. Their intention is to scare you into staying home with this number. While this number sounds high, some studies have found that the number of accidents actually goes down on days with severe weather. Why is that? One possible explanation is that with fewer people on the road, even with the dangerous conditions, the number of accidents will be less than on an “average” day. The critical lesson here is that even when the general interpretation is “accurate,” the data may not actually be evidence for the particular interpretation. This means you have no way to verify if the interpretation is in fact correct.

There is generally a comparison implied in the use of statistics. How can you make a valid comparison without having all the facts? Good question. You may have to look to another source or sources to find all the data you need.

4. Have the data been interpreted correctly?

If the author gives you their statistics, it is always wise to interpret them yourself. That is, while it is useful to read and understand the author’s interpretation, it is merely that—an interpretation. It is not the final word on the matter. Furthermore, sometimes authors (including you, so be careful) can use perfectly good statistics and come up with perfectly bad interpretations. Here are two common mistakes to watch out for:

Confusing correlation with causation. Just because two things vary together does not mean that one of them is causing the other. It could be nothing more than a coincidence, or both could be caused by a third factor. Such a relationship is called spurious.The classic example is a study that found that the more firefighters sent to put out a fire, the more damage the fire did. Yikes! I thought firefighters were supposed to make things better, not worse! But before we start shutting down fire stations, it might be useful to entertain alternative explanations. This seemingly contradictory finding can be easily explained by pointing to a third factor that causes both: the size of the fire. The lesson here? Correlation does not equal causation. So it is important not only to think about showing that two variables co-vary, but also about the causal mechanism.
Ignoring the margin of error. When survey results are reported, they frequently include a margin of error. You might see this written as “a margin of error of plus or minus 5 percentage points.” What does this mean? The simple story is that surveys are normally generated from samples of a larger population, and thus they are never exact. There is always a confidence interval within which the general population is expected to fall. Thus, if I say that the number of UNC students who find it difficult to use statistics in their writing is 60%, plus or minus 4%, that means, assuming the normal confidence interval of 95%, that with 95% certainty we can say that the actual number is between 56% and 64%.

Why does this matter? Because if after introducing this handout to the students of UNC, a new poll finds that only 56%, plus or minus 3%, are having difficulty with statistics, I could go to the Writing Center director and ask for a raise, since I have made a significant contribution to the writing skills of the students on campus. However, she would no doubt point out that a) this may be a spurious relationship (see above) and b) the actual change is not significant because it falls within the margin of error for the original results. The lesson here? Margins of error matter, so you cannot just compare simple percentages.

Finally, you should keep in mind that the source you are actually looking at may not be the original source of your data. That is, if you find an essay that quotes a number of statistics in support of its argument, often the author of the essay is using someone else’s data. Thus, you need to consider not only your source, but the author’s sources as well.

Writing statistics

As you write with statistics, remember your own experience as a reader of statistics. Don’t forget how frustrated you were when you came across unclear statistics and how thankful you were to read well-presented ones. It is a sign of respect to your reader to be as clear and straightforward as you can be with your numbers. Nobody likes to be played for a fool. Thus, even if you think that changing the numbers just a little bit will help your argument, do not give in to the temptation.

As you begin writing, keep the following in mind. First, your reader will want to know the answers to the same questions that we discussed above. Second, you want to present your statistics in a clear, unambiguous manner. Below you will find a list of some common pitfalls in the world of statistics, along with suggestions for avoiding them.

1. The mistake of the “average” writer

Nobody wants to be average. Moreover, nobody wants to just see the word “average” in a piece of writing. Why? Because nobody knows exactly what it means. There are not one, not two, but three different definitions of “average” in statistics, and when you use the word, your reader has only a 33.3% chance of guessing correctly which one you mean.

For the following definitions, please refer to this set of numbers: 5, 5, 5, 8, 12, 14, 21, 33, 38

Mean (arithmetic mean) This may be the most average definition of average (whatever that means). This is the weighted average—a total of all numbers included divided by the quantity of numbers represented. Thus the mean of the above set of numbers is 5+5+5+8+12+14+21+33+38, all divided by 9, which equals 15.644444444444 (Wow! That is a lot of numbers after the decimal—what do we do about that? Precision is a good thing, but too much of it is over the top; it does not necessarily make your argument any stronger. Consider the reasonable amount of precision based on your input and round accordingly. In this case, 15.6 should do the trick.)
Median Depending on whether you have an odd or even set of numbers, the median is either a) the number midway through an odd set of numbers or b) a value halfway between the two middle numbers in an even set. For the above set (an odd set of 9 numbers), the median is 12. (5, 5, 5, 8 < 12 < 14, 21, 33, 38)
Mode The mode is the number or value that occurs most frequently in a series. If, by some cruel twist of fate, two or more values occur with the same frequency, then you take the mean of the values. For our set, the mode would be 5, since it occurs 3 times, whereas all other numbers occur only once.

As you can see, the numbers can vary considerably, as can their significance. Therefore, the writer should always inform the reader which average they are using. Otherwise, confusion will inevitably ensue.

2. Match your facts with your questions

Be sure that your statistics actually apply to the point/argument you are making. If we return to our discussion of averages, depending on the question you are interesting in answering, you should use the proper statistics.

Perhaps an example would help illustrate this point. Your professor hands back the midterm. The grades are distributed as follows:

Grade	# Received
100	4
98	5
95	2
63	4
58	6

The professor felt that the test must have been too easy, because the average (median) grade was a 95.

When a colleague asked her about how the midterm grades came out, she answered, knowing that her classes were gaining a reputation for being “too easy,” that the average (mean) grade was an 80.

When your parents ask you how you can justify doing so poorly on the midterm, you answer, “Don’t worry about my 63. It is not as bad as it sounds. The average (mode) grade was a 58.”

I will leave it up to you to decide whether these choices are appropriate. Selecting the appropriate facts or statistics will help your argument immensely. Not only will they actually support your point, but they will not undermine the legitimacy of your position. Think about how your parents will react when they learn from the professor that the average (median) grade was 95! The best way to maintain precision is to specify which of the three forms of “average” you are using.

3. Show the entire picture

Sometimes, you may misrepresent your evidence by accident and misunderstanding. Other times, however, misrepresentation may be slightly less innocent. This can be seen most readily in visual aids. Do not shape and “massage” the representation so that it “best supports” your argument. This can be achieved by presenting charts/graphs in numerous different ways. Either the range can be shortened (to cut out data points which do not fit, e.g., starting a time series too late or ending it too soon), or the scale can be manipulated so that small changes look big and vice versa. Furthermore, do not fiddle with the proportions, either vertically or horizontally. The fact that USA Today seems to get away with these techniques does not make them OK for an academic argument.

Charts A, B, and C all use the same data points, but the stories they seem to be telling are quite different. Chart A shows a mild increase, followed by a slow decline. Chart B, on the other hand, reveals a steep jump, with a sharp drop-off immediately following. Conversely, Chart C seems to demonstrate that there was virtually no change over time. These variations are a product of changing the scale of the chart. One way to alleviate this problem is to supplement the chart by using the actual numbers in your text, in the spirit of full disclosure.

Another point of concern can be seen in Charts D and E. Both use the same data as charts A, B, and C for the years 1985-2000, but additional time points, using two hypothetical sets of data, have been added back to 1965. Given the different trends leading up to 1985, consider how the significance of recent events can change. In Chart D, the downward trend from 1990 to 2000 is going against a long-term upward trend, whereas in Chart E, it is merely the continuation of a larger downward trend after a brief upward turn.

One of the difficulties with visual aids is that there is no hard and fast rule about how much to include and what to exclude. Judgment is always involved. In general, be sure to present your visual aids so that your readers can draw their own conclusions from the facts and verify your assertions. If what you have cut out could affect the reader’s interpretation of your data, then you might consider keeping it.

4. Give bases of all percentages

Because percentages are always derived from a specific base, they are meaningless until associated with a base. So even if I tell you that after this reading this handout, you will be 23% more persuasive as a writer, that is not a very meaningful assertion because you have no idea what it is based on—23% more persuasive than what?

Let’s look at crime rates to see how this works. Suppose we have two cities, Springfield and Shelbyville. In Springfield, the murder rate has gone up 75%, while in Shelbyville, the rate has only increased by 10%. Which city is having a bigger murder problem? Well, that’s obvious, right? It has to be Springfield. After all, 75% is bigger than 10%.

Hold on a second, because this is actually much less clear than it looks. In order to really know which city has a worse problem, we have to look at the actual numbers. If I told you that Springfield had 4 murders last year and 7 this year, and Shelbyville had 30 murders last year and 33 murders this year, would you change your answer? Maybe, since 33 murders are significantly more than 7. One would certainly feel safer in Springfield, right?

Not so fast, because we still do not have all the facts. We have to make the comparison between the two based on equivalent standards. To do that, we have to look at the per capita rate (often given in rates per 100,000 people per year). If Springfield has 700 residents while Shelbyville has 3.3 million, then Springfield has a murder rate of 1,000 per 100,000 people, and Shelbyville’s rate is merely 1 per 100,000. Gadzooks! The residents of Springfield are dropping like flies. I think I’ll stick with nice, safe Shelbyville, thank you very much.

Percentages are really no different from any other form of statistics: they gain their meaning only through their context. Consequently, percentages should be presented in context so that readers can draw their own conclusions as you emphasize facts important to your argument. Remember, if your statistics really do support your point, then you should have no fear of revealing the larger context that frames them.

Important questions to ask (and answer) about statistics

Is the question being asked relevant?
Do the data come from reliable sources?
Margin of error/confidence interval—when is a change really a change?
Are all data reported, or just the best/worst?
Are the data presented in context?
Have the data been interpreted correctly?
Does the author confuse correlation with causation?

Now that you have learned the lessons of statistics, you have two options. Use this knowledge to manipulate your numbers to your advantage, or use this knowledge to better understand and use statistics to make accurate and fair arguments. The choice is yours. Nine out of ten writers, however, prefer the latter, and the other one later regrets their decision.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
Null hypothesis: Parental income and GPA have no relationship with each other in college students.
Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
Experimental
Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

Variable	Type of data
Age	Quantitative (ratio)
Gender	Categorical (nominal)
Race or ethnicity	Categorical (nominal)
Baseline test scores	Quantitative (interval)
Final test scores	Quantitative (interval)


Parental income	Quantitative (ratio)
GPA	Quantitative (interval)

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

Probability sampling: every member of the population has a chance of being selected for the study through random selection.
Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

your sample is representative of the population you’re generalizing your findings to.
your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

Will you have resources to advertise your study widely, including outside of your university setting?
Will you have the means to recruit a diverse sample that represents a broad population?
Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

Organizing data from each variable in frequency distribution tables .
Displaying data from a key variable in a bar chart to view the distribution of responses.
Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

Mean, median, mode, and standard deviation in a normal distribution

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

Mode : the most popular response or value in the data set.
Median : the value in the exact middle of the data set when ordered from low to high.
Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

Range : the highest value minus the lowest value of the data set.
Interquartile range : the range of the middle half of the data set.
Standard deviation : the average distance between each value in your data set and the mean.
Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

	Pretest scores	Posttest scores
Mean	68.44	75.25
Standard deviation	9.43	9.88
Variance	88.96	97.96
Range	36.25	45.12
	30

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

	Parental income (USD)	GPA
Mean	62,100	3.12
Standard deviation	15,000	0.45
Variance	225,000,000	0.16
Range	8,000–378,000	2.64–4.00
	653

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

Estimation: calculating population parameters based on sample statistics.
Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

A point estimate : a value that represents your best guess of the exact parameter.
An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

A test statistic tells you how much your data differs from the null hypothesis of the test.
A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

Comparison tests assess group differences in outcomes.
Regression tests assess cause-and-effect relationships between variables.
Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

A simple linear regression includes one predictor variable and one outcome variable.
A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
A z test is for exactly 1 or 2 groups when the sample is large.
An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

If you have only one sample that you want to compare to a population mean, use a one-sample test .
If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
If you expect a difference between groups in a specific direction, use a one-tailed test .
If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

a t value (test statistic) of 3.00
a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

a t value of 3.08
a p value of 0.001

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The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

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

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval

Methodology

Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Likert scale

Research bias

Implicit bias
Framing effect
Cognitive bias
Placebo effect
Hawthorne effect
Hostile attribution bias
Affect heuristic

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Introductory essay

Written by the educators who created Visualizing Data, a brief look at the key facts, tough questions and big ideas in their field. Begin this TED Study with a fascinating read that gives context and clarity to the material.

The reality of today

All of us now are being blasted by information design. It's being poured into our eyes through the Web, and we're all visualizers now; we're all demanding a visual aspect to our information...And if you're navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a clearing in the jungle. David McCandless

In today's complex 'information jungle,' David McCandless observes that "Data is the new soil." McCandless, a data journalist and information designer, celebrates data as a ubiquitous resource providing a fertile and creative medium from which new ideas and understanding can grow. McCandless's inspiration, statistician Hans Rosling, builds on this idea in his own TEDTalk with his compelling image of flowers growing out of data/soil. These 'flowers' represent the many insights that can be gleaned from effective visualization of data.

We're just learning how to till this soil and make sense of the mountains of data constantly being generated. As Gary King, Director of Harvard's Institute for Quantitative Social Science says in his New York Times article "The Age of Big Data":

It's a revolution. We're really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.

How do we deal with all this data without getting information overload? How do we use data to gain real insight into the world? Finding ways to pull interesting information out of data can be very rewarding, both personally and professionally. The managing editor of Financial Times observed on CNN's Your Money : "The people who are able to in a sophisticated and practical way analyze that data are going to have terrific jobs." Those who learn how to present data in effective ways will be valuable in every field.

Many people, when they think of data, think of tables filled with numbers. But this long-held notion is eroding. Today, we're generating streams of data that are often too complex to be presented in a simple "table." In his TEDTalk, Blaise Aguera y Arcas explores images as data, while Deb Roy uses audio, video, and the text messages in social media as data.

Some may also think that only a few specialized professionals can draw insights from data. When we look at data in the right way, however, the results can be fun, insightful, even whimsical — and accessible to everyone! Who knew, for example, that there are more relationship break-ups on Monday than on any other day of the week, or that the most break-ups (at least those discussed on Facebook) occur in mid-December? David McCandless discovered this by analyzing thousands of Facebook status updates.

Data, data, everywhere

There is more data available to us now than we can possibly process. Every minute , Internet users add the following to the big data pool (i):

204,166,667 email messages sent
More than 2,000,000 Google searches
684,478 pieces of content added on Facebook
$272,070 spent by consumers via online shopping
More than 100,000 tweets on Twitter
47,000 app downloads from Apple
34,722 "likes" on Facebook for different brands and organizations
27,778 new posts on Tumblr blogs
3,600 new photos on Instagram
3,125 new photos on Flickr
2,083 check-ins on Foursquare
571 new websites created
347 new blog posts published on Wordpress
217 new mobile web users
48 hours of new video on YouTube

These numbers are almost certainly higher now, as you read this. And this just describes a small piece of the data being generated and stored by humanity. We're all leaving data trails — not just on the Internet, but in everything we do. This includes reams of financial data (from credit cards, businesses, and Wall Street), demographic data on the world's populations, meteorological data on weather and the environment, retail sales data that records everything we buy, nutritional data on food and restaurants, sports data of all types, and so on.

Governments are using data to search for terrorist plots, retailers are using it to maximize marketing strategies, and health organizations are using it to track outbreaks of the flu. But did you ever think of collecting data on every minute of your child's life? That's precisely what Deb Roy did. He recorded 90,000 hours of video and 140,000 hours of audio during his son's first years. That's a lot of data! He and his colleagues are using the data to understand how children learn language, and they're now extending this work to analyze publicly available conversations on social media, allowing them to take "the real-time pulse of a nation."

Data can provide us with new and deeper insight into our world. It can help break stereotypes and build understanding. But the sheer quantity of data, even in just any one small area of interest, is overwhelming. How can we make sense of some of this data in an insightful way?

The power of visualizing data

Visualization can help transform these mountains of data into meaningful information. In his TEDTalk, David McCandless comments that the sense of sight has by far the fastest and biggest bandwidth of any of the five senses. Indeed, about 80% of the information we take in is by eye. Data that seems impenetrable can come alive if presented well in a picture, graph, or even a movie. Hans Rosling tells us that "Students get very excited — and policy-makers and the corporate sector — when they can see the data."

It makes sense that, if we can effectively display data visually, we can make it accessible and understandable to more people. Should we worry, however, that by condensing data into a graph, we are simplifying too much and losing some of the important features of the data? Let's look at a fascinating study conducted by researchers Emre Soyer and Robin Hogarth . The study was conducted on economists, who are certainly no strangers to statistical analysis. Three groups of economists were asked the same question concerning a dataset:

One group was given the data and a standard statistical analysis of the data; 72% of these economists got the answer wrong.
Another group was given the data, the statistical analysis, and a graph; still 61% of these economists got the answer wrong.
A third group was given only the graph, and only 3% got the answer wrong.

Visualizing data can sometimes be less misleading than using the raw numbers and statistics!

What about all the rest of us, who may not be professional economists or statisticians? Nathalie Miebach finds that making art out of data allows people an alternative entry into science. She transforms mountains of weather data into tactile physical structures and musical scores, adding both touch and hearing to the sense of sight to build even greater understanding of data.

Another artist, Chris Jordan, is concerned about our ability to comprehend big numbers. As citizens of an ever-more connected global world, we have an increased need to get useable information from big data — big in terms of the volume of numbers as well as their size. Jordan's art is designed to help us process such numbers, especially numbers that relate to issues of addiction and waste. For example, Jordan notes that the United States has the largest percentage of its population in prison of any country on earth: 2.3 million people in prison in the United States in 2005 and the number continues to rise. Jordan uses art, in this case a super-sized image of 2.3 million prison jumpsuits, to help us see that number and to help us begin to process the societal implications of that single data value. Because our brains can't truly process such a large number, his artwork makes it real.

The role of technology in visualizing data

The TEDTalks in this collection depend to varying degrees on sophisticated technology to gather, store, process, and display data. Handling massive amounts of data (e.g., David McCandless tracking 10,000 changes in Facebook status, Blaise Aguera y Arcas synching thousands of online images of the Notre Dame Cathedral, or Deb Roy searching for individual words in 90,000 hours of video tape) requires cutting-edge computing tools that have been developed specifically to address the challenges of big data. The ability to manipulate color, size, location, motion, and sound to discover and display important features of data in a way that makes it readily accessible to ordinary humans is a challenging task that depends heavily on increasingly sophisticated technology.

The importance of good visualization

There are good ways and bad ways of presenting data. Many examples of outstanding presentations of data are shown in the TEDTalks. However, sometimes visualizations of data can be ineffective or downright misleading. For example, an inappropriate scale might make a relatively small difference look much more substantial than it should be, or an overly complicated display might obfuscate the main relationships in the data. Statistician Kaiser Fung's blog Junk Charts offers many examples of poor representations of data (and some good ones) with descriptions to help the reader understand what makes a graph effective or ineffective. For more examples of both good and bad representations of data, see data visualization architect Andy Kirk's blog at visualisingdata.com . Both consistently have very current examples from up-to-date sources and events.

Creativity, even artistic ability, helps us see data in new ways. Magic happens when interesting data meets effective design: when statistician meets designer (sometimes within the same person). We are fortunate to live in a time when interactive and animated graphs are becoming commonplace, and these tools can be incredibly powerful. Other times, simpler graphs might be more effective. The key is to present data in a way that is visually appealing while allowing the data to speak for itself.

Changing perceptions through data

While graphs and charts can lead to misunderstandings, there is ultimately "truth in numbers." As Steven Levitt and Stephen Dubner say in Freakonomics , "[T]eachers and criminals and real-estate agents may lie, and politicians, and even C.I.A. analysts. But numbers don't." Indeed, consideration of data can often be the easiest way to glean objective insights. Again from Freakonomics : "There is nothing like the sheer power of numbers to scrub away layers of confusion and contradiction."

Data can help us understand the world as it is, not as we believe it to be. As Hans Rosling demonstrates, it's often not ignorance but our preconceived ideas that get in the way of understanding the world as it is. Publicly-available statistics can reshape our world view: Rosling encourages us to "let the dataset change your mindset."

Chris Jordan's powerful images of waste and addiction make us face, rather than deny, the facts. It's easy to hear and then ignore that we use and discard 1 million plastic cups every 6 hours on airline flights alone. When we're confronted with his powerful image, we engage with that fact on an entirely different level (and may never see airline plastic cups in the same way again).

The ability to see data expands our perceptions of the world in ways that we're just beginning to understand. Computer simulations allow us to see how diseases spread, how forest fires might be contained, how terror networks communicate. We gain understanding of these things in ways that were unimaginable only a few decades ago. When Blaise Aguera y Arcas demonstrates Photosynth, we feel as if we're looking at the future. By linking together user-contributed digital images culled from all over the Internet, he creates navigable "immensely rich virtual models of every interesting part of the earth" created from the collective memory of all of us. Deb Roy does somewhat the same thing with language, pulling in publicly available social media feeds to analyze national and global conversation trends.

Roy sums it up with these powerful words: "What's emerging is an ability to see new social structures and dynamics that have previously not been seen. ...The implications here are profound, whether it's for science, for commerce, for government, or perhaps most of all, for us as individuals."

Let's begin with the TEDTalk from David McCandless, a self-described "data detective" who describes how to highlight hidden patterns in data through its artful representation.

David McCandless

The beauty of data visualization.

i. Data obtained June 2012 from “How Much Data Is Created Every Minute?” on http://mashable.com/2012/06/22/data-created-every-minute/ .

Relevant talks

Hans Rosling

The magic washing machine.

Nathalie Miebach

Art made of storms.

Chris Jordan

Turning powerful stats into art.

Blaise Agüera y Arcas

How photosynth can connect the world's images.

The birth of a word

Entertainment
Environment
Information Science and Technology
Social Issues

Home Essay Samples Science

Essay Samples on Statistics

The importance of statistics in daily life: quantifying reality.

Statistics may seem like an abstract field reserved for researchers and analysts, but its influence extends far beyond academic settings. In fact, statistics play a vital role in shaping our daily lives, informing decision-making, guiding policies, and helping us make sense of complex information. From...

Models and Methods of Wind Speed Forecasting

Wind power energy has a significant importance in the electric grid. As it is an inexhaustible and freely available resource, its usage is globally increasing. It is replacing all other costly exhaustible resources that were used so far. So, accurate prediction of wind power is...

Two Types of Statistics: Descriptive and Inferential

Statistics is sub field of both science and mathematics as it uses the rules from these two fields. It works with the calculations and quantifications of data, scrutinizing it, elucidating, and dispensing it in the best form. The students of this subject collect the data...

Introduction To Statistics: Definition, Benefits, Methods

Definition Branch of mathematics concerned with collection, classification, analysis, and interpretation of numerical facts, for drawing inferences on the basis of their quantifiable likelihood (probability). Statistics can interpret aggregates of data too large to be intelligible by ordinary observation because such data (unlike individual quantities)...

Qualitative Research

International Business' Statistics of Import and Manufacturing Process

Introduction Cambridge dictionary defines international business as the activity of trading goods and services between countries. However international business is beyond this definition, it has a very wide scope. Basically, international business is a cross border transaction between individuals, business or government entities. The transaction...

International Business

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Statistics Issues Suitable for Academic Debate

If you’re struggling with your essay writing task in the field of statistics, you have to start with a winning topic. Using our specific statistics essay topics for inspiration, you may come up with a brand-new issue to discuss in your essay or choose one from the list and work on it from A to Z:

The Results of the Collaborative Work Between Egon Pearson and Jerzy Neyman
Importance of Statistics and Mathematics to Economics
Dynamic Bradley-Terry Modeling of Sports Tournaments
A Statistical Analysis of Crime Offenses Recorded in Nevada
The Law of Large Numbers (LLN) to Guarantee Stable Long-Term Results for the Averages of Some Random Events
Importance of Statistics in the Area of Educational Management
Statistics, Estimators and Pivotal Quantities
Stochastic Music by Iannis Xenakis: Predicative Ways to Create Art
Advantages and Disadvantages of Official Statistics in the Research in the Sociology Field
Bayesian Probability as an Interpretation of the Concept of Probability
Analysis of Loss Systems with Overlapping Resource Requirements
Measurement Processes That Generate Statistical Data
Early Applications of Statistical Thinking in the 17th Century
Statistical Modeling of Swimming Microorganisms
Draft Statistics on Health Care Prescription Errors in the USA
Definition and Meaning of a Statistical Error
Sir Arthur Lyon Bowley, the Pioneer of the Use of Sampling Techniques in Social Surveys
Three Kinds of Lies: Lies, Damned Lies, and Statistics
Methods of Statistics Combined with Chaos Theory and Fractal Geometry
The Relationship Between Dependent (Output) Variables and Independent (Input) Variables

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Statistic - Free Essay Examples and Topic Ideas

Statistics is the branch of mathematics that deals with collecting, analyzing, interpreting, and presenting data. It involves the use of methods to organize and summarize large amounts of information, draw conclusions from data, and make informed decisions based on the results. Statistics can help us understand trends and patterns in data, identify relationships between variables, and make predictions about future events. It is applied in various fields such as science, business, medicine, and social sciences to make sense of complex data and provide valuable insights.

📘 Free essay examples for your ideas about Statistic
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Swanage and Studland
Sports Statistician
Air Pollution Statistics
Advantages and Disadvantages of Being an Actuary
Chapter 7 Exam Review. Solve the problem.
Intro to Statistics
Descriptive Statistics
lies,damned lies and statistics
Statistical methods in medical research
Statistics – Types of Surveys
Bilateral Radiological Involvement
Practical Application of Statistics in Nursing
Accidental Shooting Death Statistics
Statistics of Clinical Cases of Systemic Infection
Fast Food Statistics
Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists
How to Lie With Statistics?
The Role of Research and Statistics in the Field of Psychology
Simulation Using Probability
Modernization of Income System
The Statistical “Probability of Love at First Sight”Summary
Earthquake Statistics and Prediction
Advantages And Disadvantages Of Official Statistics In Sociological Research
Obesity Statistics
Importance of Statistics in Educational Management
What Are Descriptive Statistics?
Species Accumulation Curves
Macroeconomics Economics Commentary – patterns in America’s unemployment statistics
Obesity Statistics in USA
Statistics: Credit Card and Pelican Stores
The Two-Point Threshold
Uses of Statistical Information in Medicine
Uses of Statistics in Healthcare

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Math Article

Statistics Questions

Statistics questions based on CBSE syllabus and NCERT guidelines are provided here for students. These questions cover the syllabus of Statistics concept for Class 9, 10 and 11. Practising these problems will help students to score better on this topic. Also, it will help them to participate in competitive exams. Also, find probability questions here at BYJU’S.

Definition : Statistics is a branch of mathematics concerned with collecting meaningful data or information.

Also, read: Statistics

The data collected to define a state or condition.

Questions on Statistics with Answers

1. Give any two examples of collecting data from day-to-day life.

A. Increase in population of our country in the last two decades.

B. Number of tables and chairs in a classroom

After the collection of data, when we represent them in the form of table or chart or any other means, which help us to have a quick glance over the data, it is said to be its presentation. It also means a rearrangement of raw data in a particular order.

2. If marks obtained by students in a class test is given as per below:

55 36 95 73 60 42 25 78 75 62

Then arrange the marks from lowest to highest.

Solution: We need to arrange the marks obtained by each student in ascending order:

25 36 42 55 60 62 73 75 78 95

3. Check the following frequency distribution table, consisting of weights of 38 students of a class:

(i) What is class-interval for classes 31 – 35?

(ii) How many students are there in the range of 41-45 kgs?

(i) Class interval = Upper class limit – lower class limit

(ii) For the 41-45 range, there are 14 students.

The pictorial representation of data, in the form of vertical or horizontal rectangular bars.

A set of rectangles with bases along with the intervals between class boundaries and with areas proportional to frequencies in the corresponding classes.

: It is used to compare sets of data or to show a cumulative frequency distribution. It uses a line graph to represent quantitative data.

(range) = Upper class limit – lower class limit

= (Upper class limit + lower class limit)/2

4. A family with a monthly income of ` 20,000 had planned the following expenditures per month under various heads:

Draw a bar graph for the data above.

5. In a city, the weekly observations made in a study on the cost of the living index are given in the following table:

Draw a frequency polygon for the data above.

Solution: Class- mark = (Upper class limit-lower class limit)/2 = (150 + 140)/2 = 290/2 = 145

Thus we can create a new table with class-mark.


140-150	145	5
150-160	155	10
160-170	165	20
170-180	175	9
180-190	185	6
190-200	195	2
Total		52

Now with these class marks we can plot the frequency polygon as shown below.

Mean: The average of number of observations given.

Mode: The mode is the value of the observation occuring most frequently or repeating. An observation with the maximum frequency is called the mode.

Median: The median which divides the given observation into exactly two parts.

6. Consider a small unit of a factory where there are 5 employees : a supervisor and four labourers. The workers earn a salary of Rs. 5,000 per month each while the supervisor gets Rs. 15,000 per month. Calculate the mean, median and mode of the salaries.

Mean = (5000 + 5000 + 5000 + 5000 + 15000)/5 = 35000/5 = 7000

So, the mean salary is Rs. 7000 per month

To obtain the median, let us arrange the salaries in ascending order:

5000, 5000, 5000, 5000, 15000

Median = (n+1)/2 = (5+1)/2 = 6/2 = 3rd observation

Median = Rs. 5000/-

Mode = Number of times an observation is repeated = Rs.5000/-

7. The distribution in the table below shows the number of wickets taken by bowlers in one-day cricket matches. Find the mean number of wickets using the correct method. What does the mean signify?

Solution: Here, the class size varies, and the class marks (x i ) are large. Apply the step deviation method for a = 200 and h = 20.

			=x -200	=d /20	f
20-60	7	40	-160	-8	-56
60-100	5	80	-120	-6	-30
100-150	16	125	-75	-3.75	-60
150-250	12	200	0	0	0
250-300	2	300	100	5	10
350-450	3	400	200	10	30
Total	45				-106

So, ʉ = -106/45

x̄ = 200+20(-106/45) = 200 – 47.11 = 152.89

Hence, on an average, the number of wickets taken by these 45 bowlers in one-day cricket is 152.89.

8. A survey conducted on 20 houses in an area by a group of people resulted in the subsequent frequency table for the number of family members in a house:

Find the mode of this data.

Solution: Here the maximum class frequency = 8,

Class corresponding to this frequency = 3 – 5.

So, the modal class = 3 – 5.

Modal class = 3 – 5, lower limit (l) of modal class = 3, class size (h) = 2

Frequency (f 1 ) of the modal class = 8

Frequency (f 0 ) of class preceding the modal class = 7,

Frequency (f 2 ) of class succeeding the modal class = 2.

Now, let us put these values in the formula :

Therefore, the mode of the data above is 3.286.

l = lower limit of median class,

n = number of observations,

cf = cumulative frequency of class preceding the median class,

f = frequency of median class,

h = class size

9. A survey regarding the heights (in cm) of 51 girls of Class X of a school was conducted and the following data were obtained:

Find the median height.

Solution: Observe the below table:

Height (in cm)	Frequency	Cumulative frequency
>140	4	4
>145	7	11
>150	18	29
>155	11	40
>160	6	46
>165	5	51

n/2 = 51/2 = 25.5

This observation lies in the class 145 – 150.

l (the lower limit) = 145,

cf (the cumulative frequency of the class preceding 145 – 150) = 11

f (the frequency of the median class 145 – 150) = 18,

h (the class size) = 5

Using the formula,

So, the median height of the girls is 149.03 cm.

Important Questions Class 9 Maths Chapter 14 Statistics
Important Questions Class 10 Maths Chapter 14 Statistics
Important Questions Class 11 Maths Chapter 15 Statistics

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Essay on Students

Statistics Questions Essay

Type of paper: Essay

Topic: Students , Study , Value , Population , Theory , Time , Hypothesis , Distribution

Words: 1100

Published: 11/12/2021

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a. When a researcher studies a process or a phenomenon, it is impossible to involve the whole population in the analysis, therefore the certain assumptions and limitations are necessary. Supposing, the population has the mean μ and the standard deviation σ, and the quantity N. The sampling distribution is the smaller sample with the quantity n (N > n), taken from the population (part of the population), with μ that is equal to the population mean (μ=μ) and the standard deviation, that is calculated as: σM=σn. The sampling distribution of the mean approximately represents the properties of the population and can be used for population study and testing he statistical hypothesis. b. Result 1. If the sample distribution with the quantity n is taken, the distribution of the mean approaches to the normal distribution. The greater the number of n is, the more the distribution approaches the normal. Therefore, all the properties of the normal distribution can be used to describe a sample. Result 2. The mean of the sampling distribution is approximately equal to the mean of the normal distribution. Thus, if the researcher takes several samples, their means are equal and approach the population mean. c. All the researches in statistics deal with the samples and their characteristics. Therefore, the descriptive statistics are obtained, hypothesis are set and tested for the sample distributions, not the population characteristics. Furthermore, in many cases it is impossible to obtain the population means and variance, for example when social research is performed. The central limit theorem provides a scientific basis for the approximation of the sample distribution characteristics to the normal distribution (population) and allows applying the properties of the normal distribution to the samples, as well as testing hypotheses with the sampling distributions. d. The mean of the population is μ = 500 ml, the standard deviation is σ = 14 ml. The mean fill for the first range is μ1 = 497 ml and the mean fill for the second range is μ2 = 502 ml. The probability that the can is filled with the certain value is obtained basing on the z-values. The z-value calculates: z=μ-μσ. Therefore, z1=497-50014=-0.21; z2=502-50014=0.14. The probabilities are obtained from the z-table. These are the probabilities that the can is filled less than μ volume, and it is noted as P(X< μ). The probability that the can is filled less than 497 ml is: P(X < 497) = 0.4168. The probability that the can is filled less than 502 ml is: P(X < 502) = 0.5557. Thus, the probability that the can is filled between 497 and 502 ml is P (497 < X < 502) = P(X < 502) - P(X < 497) = 0.5557 - 0.4168 = 0.1389. The probability of the can being filled between 497 and 502 ml is 0.1389, or there is a 13.89 % chance that a can is filled with the volume 497 - 502 ml.

a. The mean study time is μ=50 hours, and the standard deviation is σM=8 hours. The sample size, which is the number of students studying in the course is n = 50. Therefore, the margin of error calculates as: ME = z · σn . ME = 1.96 · 850= 2.21. The value of the weekly study hours in the Business School at Island University is 50 ± 2.21 hours. b. We assume that the sample taken for the research is normal. This assumption is true since the number of students in the study is rather high (n = 50), therefore the distribution of the mean is close to the normal. For 95% confidence interval we take z-value, z = 1.96. The confidence interval is constructed basing on the mean and margin of error values: Mean ± ME. Then, the range stands for the interval to which the mean value belongs to. c. The 95% confidence interval informs us that the students spend between 47.78 and 52.21 hours per week studying in the Business School at Island University. Having constructed the confidence interval, we can be 95% sure that the value of the study hours at this university is within 47.78 and 52.21 hours.

a. The mean study time in all universities is μ =46 hours (parameter), and it is the parameter value. The mean time of the weekly study time is the statistic value. To test whether the full-time business students at Island University study longer than full-time business students at other universities, it is necessary to set up hypothesis. The null hypothesis H0: the weekly study time of business students at Island University is the same as weekly study time of business students at other universities; or the weekly study hours are equal: μ=μ. The μ value is the statistic value. The alternate hypothesis is opposite to the null. The alternate hypothesis H1: the weekly study time of business students at Island University is longer than the weekly study time of business students at other universities; or the weekly study hours at Island University are greater: μ>μ.

The hypothesis is tested with the calculation of the test statistic. For this case, the t-test is calculated:

t=Statistic-Parameterσ=50-468=0.5 Then, the test statistic is compared to the critical value. The critical value is a table value for alpha level 0.05. The value is tcrit(0.05;49) = 1.6. The appropriate hypothesis is accepted basing on the comparison of the t and tcrit values. If t < tcrit, then the null hypothesis is accepted, and alternate hypothesis is rejected; if t > tcrit, the alternate hypothesis is accepted, and null hypothesis is rejected. Since t < tcrit (0.5 < 1.6), then null hypothesis is accepted, and μ=μ. b. The study time for students from Island University and other universities are normally distributed values. The t-value is chosen for one-sided t-test with alpha level 0.05. One-sided t-test is applied because the mean value of the sample is tested for being greater than the population value. On the contrary, two-sided test is applied when the sample mean value is tested for being smaller and greater than the population mean. c. The statistical test is a scientific proof that the weekly study time of business students at Island University is the same as weekly study time of business students at other universities. In other words, there is no significant difference between the study hours of business students at Island University and other universities. The test proves that the statement of the Business school at Island University is groundless.

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Live IELTS Statistics

The presented data visualisation about topics and question types is updating online as every new IELTS question is added to our database. As all questions are not official (they are reported by exam-takers and gathered from the internet), the statistics based on them could represent an incomplete view; however, due to the huge number of samples, the resulting error is likely minimal, and we can assume the overall picture is accurate.

Let's start with the analysis of topic distribution for the whole exam — both, speaking and writing tasks:

Overall IELTS topic statistics

The pie chart above illustrates real-time cumulative data about IELTS question topics from both parts of the exam: Speaking and Writing. Overall, the exam themes are nicely diversified — there is no dominant theme.

You will notice 7 main topics with around 10% each and three of them could be considered the core themes of IELTS — Education , Entertainment , and Places . With Work and Health and Diet , they hold half of the pie. It simply means there is a 50% probability that you will meet one of these topics in your IELTS exam.

With about 10% each, Storytelling and Life are special task categories, and, in fact, might be about any topic that is not included in other sections. The only difference is that Storytelling includes tasks where you should tell a story (Speaking part 2 or Writing part 1 Letter), rather than describe something or share an opinion.

Nevertheless, if you take a look at particular parts of the IELTS test, you'll see that they are not as consistent:

IELTS subpart topic statistics

Ielts letter (writing task 1) topic data, ielts essay (writing task 2) topic data, ielts speaking topic data.

Okay, now let's look at writing task type distribution:

IELTS Writing Question Types

Ielts letters (writing task 1) question type data.

When it comes to IELTS letters, nearly half of all tasks are of the Giving Information type. In the middle, Request and Complain have about 20% probability each, which means two of five random letter tasks will be from these categories. With a paltry 5% each, four other types — Apology , Invitation , Thanking , and Applying for a Job — win the third place.

IELTS Essays (Writing Task2) question type data

IELTS essay type distribution is more smooth; however, the major type — Argument Essay — also keeps a hefty number (roughly 50%) of all Writing Task 2 questions. The second most frequent type, Opinion Essay , is half as common. The Two-question Essay type represents about 15% of the pie. Finally, 1-in-10 essays are the Problem-and-Solution Essay , which is the rarest type, but it is still significant enough to practice.

Test Type Statistics: Academic IELTS vs General IELTS

IELTS Academic vs IELTS General statistics - what is more popular

Unsurprisingly, in terms of the exam type popularity, Academic IELTS , without a doubt, is the leader — three-fourths of all IELTS-takers go for the Academic type of the exam. General IELTS has the remaining 25%. It is not some shocking fact, because there are more test takers who take the exam for an application to a school or university than for other purposes.

Motivation statistics: reasons for taking IELTS

Charts below were calculated by IELTS officials and published on ielts.org.

There is something interesting in the charts below: the IELTS band distributions for different brackets based on the purpose for taking the test. The purposes are declared by test takers themselves (in a questionnaire that you fill while registering for the exam).

Academic IELTS takers' scores

Academig IELTS score statistics - scores for different test-takers grouped by purpose

First of all, people who declared immigration or employment as a purpose have the best score distributions for this type of exam. However, the number of people in these groups should be comparably low, as it is super rare to use the Academic IELTS for a job or immigration application. Meanwhile, IELTS candidates doing the test for educational goals (the most significant group in Academic exam test-takers) have markedly lower scores.

The cut-off IELTS bands for bachelor and master admissions usually lie in a range of 6 to7.5. The graph shows that only half of candidates got the six-and-higher score and about one-fourth had less than 5.5. (It is perhaps also important to remember that when applying for international studies, you may also need a certain IELTS score for your visa, too.)

General IELTS takers' scores

General IELTS scores for different test-takers grouped by purpose

What is markedly noticeable is that General scores are, on average, stronger, and future immigrants and doctors (but not nurses!) perform the best. More than four-fifths of them have a band higher than 6.

Overall, the main facts are:

Academic IELTS is a bit more difficult — General IELTS band distributions are shifted by nearly 0.5 points towards the higher scores, compared to Academic IELTS.
Do not even think you receive band 9 in IELTS — statistics says this mission is almost impossible.
If you want to get a higher band score, become a dentist and decide to immigrate ! — We can safely assume from this analysis that people who decided to immigrate or want to work as doctors in English-speaking countries are the most motivated candidates and this leads to higher IELTS results.

Congratulations! Now, after reading these insights, IELTS has become clearer for you, and we hope you will use this information to enhance your effectiveness in IELTS preparation with ielts777.com .

IELTS videos (tips, strategies, and mock tests):

Improve your ielts speaking in just 60 minutes — ielts preparation videos, ielts listening tips — ielts preparation videos, how to talk about your free time and hobbies in english — ielts preparation videos, 1 simple trick to become fluent in english — ielts preparation videos, ielts speaking score 8.5 – india — ielts speaking videos, ielts newsletter.

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Analyzing Roberto Clemente’s Stellar Baseball Career through his Statistics

This essay about Roberto Clemente highlights his remarkable impact on baseball as a symbol of excellence and resilience. It traces his rise from Puerto Rico to his legendary career with the Pittsburgh Pirates, showcasing his outstanding batting average and defensive skills. Beyond his athletic achievements, the essay underscores Clemente’s humanitarian efforts and lasting legacy, including his posthumous induction into the Baseball Hall of Fame and the retirement of his number 21.

How it works

Roberto Clemente, a name forever etched in the rich history of baseball, stands as a beacon of excellence and resilience. His illustrious career, a tapestry of unparalleled achievements and unforgettable milestones, continues to inspire athletes and fans across generations. To truly appreciate the profound impact Clemente had on the sport, one must delve into the detailed statistics that marked his extraordinary journey.

Born in Carolina, Puerto Rico, on August 18, 1934, Clemente’s ascent to baseball greatness was far from ordinary. Signed by the Brooklyn Dodgers at the youthful age of 18, his path would lead him to transcend the sport and become a symbol of unparalleled excellence.

However, it was his time with the Pittsburgh Pirates that truly highlighted his exceptional talents.

Clemente’s offensive prowess was apparent from the beginning, as he consistently displayed remarkable batting skills throughout his career. His batting average, a cherished metric among baseball aficionados, showcased his consistency and skill at the plate. With a career batting average of .317, Clemente secured his place among the elite hitters of his generation. Yet, beyond the statistics, it was his fluid and powerful swing that left an enduring impression on the sport.

Clemente’s contributions extended well beyond his offensive capabilities. Renowned for his defensive brilliance, he dominated the outfield with a blend of speed, agility, and precision that was unparalleled. His collection of 12 Gold Glove Awards stands as a testament to his defensive skills, reminding us of his extraordinary abilities. Clemente’s defensive acumen not only prevented countless runs but also set a new standard for outfield play in Major League Baseball.

However, mere statistics cannot fully capture the essence of Clemente’s impact on baseball. His influence transcended the game, serving as a source of hope and inspiration for millions worldwide. Clemente’s unwavering commitment to humanitarian efforts, especially his dedication to aiding those in need in Latin America, highlighted his compassion and generosity. Tragically, it was during a humanitarian mission that he lost his life, perishing in a plane crash while delivering aid to earthquake-stricken Nicaragua on December 31, 1972.

In the wake of his untimely death, Clemente’s legacy only grew stronger. He was posthumously inducted into the Baseball Hall of Fame in 1973, becoming the first Latin American player to receive this prestigious honor. The Pittsburgh Pirates’ retirement of his iconic number 21 further cemented his memory, serving as a poignant reminder of his lasting impact on the sport.

Reflecting on Roberto Clemente’s remarkable baseball career through the lens of statistics reminds us not only of his on-field brilliance but also of the profound humanity that defined him. Beyond the numbers lies a story of resilience, passion, and unwavering dedication—a story that continues to inspire countless individuals to this day. Roberto Clemente, a true baseball legend, will forever hold a place in the hearts of fans worldwide, his legacy a timeless reminder of the power of sport to transcend boundaries and unite us all.

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Home — Blog — Topic Ideas — Top 60 Satire Topics for Thought-Provoking and Humorous Essays

Understanding Satire

Historical Background

Satire has a rich history, dating back to ancient Greek and Roman literature. Notable satirists like Aristophanes, Juvenal, and Horace used humor and irony to critique their societies. In modern times, figures like Jonathan Swift, Mark Twain, and George Orwell have continued this tradition, using satire to highlight societal issues and provoke thought.

Elements of Satire

Satire relies on several key elements to be effective:

Irony : Using words to convey a meaning opposite to their literal meaning.
Exaggeration : Amplifying characteristics or situations to absurd levels.
Parody : Imitating the style of a particular genre, person, or work for comedic effect.
Wit : Clever and humorous expression of ideas.

How to Write a Satire Essay

Choosing a Topic

Choosing a relevant and relatable topic is crucial for a successful satire essay. Look for subjects that resonate with current events or common experiences. Consider brainstorming with peers or conducting research to find issues that provoke strong opinions or emotions.

Research and Planning

Thorough research is essential to understand the subject and develop insightful commentary. Create an outline to organize your thoughts and ensure a logical flow of ideas. Identify the key points you want to satirize and how you will use humor to highlight them.

Writing Techniques

Incorporate satire effectively by balancing humor with critical commentary. Use irony, exaggeration, and parody to emphasize the absurdities of your topic. Ensure your humor is clear and does not overshadow the message you intend to convey.

60 Satire Essay Topics for Students

Good satire topics.

The Social Media Influencer Phenomenon : Satirize the rise of influencers and their impact on society, focusing on the absurdity of their influence on lifestyle and consumer habits.
Political Campaign Promises : Highlight the empty promises made during political campaigns and the gullibility of voters who believe them.
Diet Fads and Health Trends: Critique the endless cycle of diet fads and the often contradictory health trends that people blindly follow.
Standardized Testing in Schools : Exaggerate the emphasis on standardized testing and its negative effects on students and teachers.
Climate Change Denial: Satirize the refusal to accept climate change and the ridiculous arguments made by deniers.
Celebrity Culture and Obsession: Examine the obsession with celebrities and their personal lives, highlighting the absurdity of idolizing people simply because they are famous.
The Tech Addiction Epidemic: Critique society’s dependence on technology and the way it dominates every aspect of our lives, from communication to entertainment.
Reality TV Show Absurdities: Highlight the ridiculous nature of reality TV shows and how they often distort reality for entertainment purposes.
The Job Market for College Graduates: Satirize the challenges faced by recent graduates in finding employment, focusing on the unrealistic expectations and underpaid internships.
Corporate Greenwashing: Critique the superficial efforts of companies to appear environmentally friendly, exposing the hypocrisy behind their marketing campaigns.

Funny Satire Topics

The Unwritten Rules of Social Media: Satirize the absurd and often contradictory etiquette of social media platforms, such as the pressure to like and comment on every post.
The "Perfect" Instagram Life: Highlight the hilarity of people curating their lives for Instagram, focusing on the lengths they go to create a façade of perfection.
Online Dating Profiles: Exaggerate the differences between real-life personalities and online dating profiles, poking fun at the embellishments people use to attract matches.
The Hipster Lifestyle : Critique the ironic and often pretentious elements of hipster culture, including their obsession with vintage items and artisanal everything.
Office Buzzwords: Satirize the overuse of corporate jargon and buzzwords in the workplace, making fun of phrases like "synergy" and "think outside the box."
The Fitness Guru: Highlight the absurdity of extreme fitness trends and the self-proclaimed fitness gurus who promote them on social media.
Parenting Fads: Critique the ever-changing trends in parenting advice, from helicopter parenting to free-range kids, emphasizing the humorous contradictions.
The Vacation Photo Overload: Exaggerate the obsession with documenting every moment of a vacation on social media, making fun of the staged photos and endless selfies.
Life Hacks Gone Wrong: Poke fun at the ridiculous and often impractical life hacks that flood the internet, highlighting their sometimes disastrous results.
The DIY Craze: Satirize the do-it-yourself culture, focusing on the comedic failures that often result from overly ambitious DIY projects.

Satire Essay Topics on Politics

The Endless Election Campaign : Satirize the never-ending political campaigns and their impact on society.
Political Promises : Highlight the absurdity of politicians' promises that are rarely fulfilled.
Social Media Politics : Critique how politicians use social media for their campaigns and the resulting effects on public discourse.
The Watergate Scandal : Draw parallels between historical political scandals and contemporary politics.
The Cold War : Satirize the tensions and propaganda of the Cold War era in a modern context.
Monarchies and Democracies : Compare the absurdities of ancient monarchies with modern democratic practices.
The Perpetual Politician: Satirize the career politicians who stay in office for decades without significant accomplishments.
The Blame Game: Highlight the absurdity of politicians constantly blaming their predecessors for current issues.
Government Shutdowns: Critique the frequent government shutdowns and their effects on public services and employees.
Political Debates: Satirize the theatrical nature of political debates and the lack of substantive discussion.

Satire Essay Topics on Social Issues

The Social Media Influencer : Satirize the rise of influencers and their impact on youth and culture.
Tech Addiction : Highlight society's dependence on technology and its consequences.
Privacy in the Digital Age : Critique the erosion of privacy in a world dominated by social media and surveillance.
Celebrity Culture : Satirize the obsession with celebrities and their influence on public behavior.
Diet Fads : Critique the endless cycle of diet trends and their impact on health.
Reality TV : Highlight the absurdity of reality television and its effect on viewers' perceptions of reality.
The Charity Gala: Critique the extravagance of charity events that spend more on the event than the cause.
Online Outrage: Satirize the culture of outrage and canceling people over minor infractions on social media.
Parenting Trends: Highlight the absurdity of constantly changing parenting fads and their supposed benefits.
Gentrification : Critique the process of gentrification and its impact on original residents and local culture.

Satire Essay Topics on Education

Standardized Testing : Satirize the emphasis on standardized testing and its impact on education quality.
Homework Overload : Critique the excessive homework assigned to students and its effects on their well-being.
School Uniforms : Highlight the absurdity of strict school uniform policies and their supposed benefits.
The College Admissions Game : Satirize the competitive and often unfair college admissions process.
Unpaid Internships : Critique the expectation of unpaid internships as a necessary step to career success.
Student Debt Crisis : Highlight the absurdities of the student loan system and its impact on graduates.
Virtual Learning: Satirize the challenges and absurdities of online education during the pandemic.
Teacher Evaluations: Critique the often unrealistic and overly critical evaluations teachers face.
The Grade Inflation: Highlight the absurdity of grade inflation and its impact on student motivation and learning.
College Rankings: Satirize the obsession with college rankings and their influence on students' and parents' choices.

Satire Essay Topics on Environmental Issues

Corporate Greenwashing : Satirize companies that falsely advertise their products as environmentally friendly.
Plastic Ban : Critique the effectiveness of plastic bans and their real impact on the environment.
Climate Change Denial : Highlight the absurdity of denying climate change in the face of overwhelming evidence.
Big Oil's Green Initiatives : Satirize the contradictory nature of fossil fuel companies promoting green initiatives.
Fast Fashion : Critique the environmental impact of the fast fashion industry and consumer habits.
Recycling Myths : Highlight the misconceptions and inefficiencies in the recycling system.
Eco-Friendly Celebrities : Satirize celebrities who promote environmentalism but live extravagant, wasteful lifestyles.
Carbon Offsetting: Critique the effectiveness and sincerity of carbon offsetting programs.
The Organic Craze: Highlight the absurdities and misconceptions surrounding the organic food movement.
Electric Car Hype: Satirize the promotion of electric cars as the ultimate solution to environmental problems without addressing broader issues.

Writing and Refining Your Satire Essay

Drafting and Revising

Writing a satire essay requires multiple drafts to refine humor and ensure clarity. Seek feedback from peers to gauge the effectiveness of your satire. Revise your essay to improve the flow of ideas and enhance comedic elements.

Common Pitfalls to Avoid

Avoid crossing the line from satire to offense. Ensure your satire is humorous without being harmful or disrespectful. Maintain a balance between humor and insightful critique to keep your essay engaging and meaningful.

The Power of Satire

Satire is a powerful tool for social commentary, offering a unique way to highlight and critique societal issues. By using humor and irony, satire can provoke thought and inspire change.

Final Thoughts

Writing a satire essay is both challenging and rewarding. It requires creativity, critical thinking, and a keen sense of humor. By exploring a wide range of satire topics, from politics and social issues to education and environmental concerns, students can find inspiration for their essays. Don’t hesitate to experiment with different satire ideas and techniques to make your essay engaging and impactful.

Satirical topics allow writers to delve into current events and cultural trends, using humor to reflect on the absurdities of society. Whether you are a high school student looking for satire topics for high school projects or a college student seeking good satire topics for a class assignment, there is a wealth of material to explore. Funny satire topics can make your essay entertaining, while also providing a critical perspective on important issues.

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Takeaways from AP analysis of Gaza Health Ministry’s death toll data

FILE - Palestinians search for bodies and survivors in the rubble of a residential building destroyed in an Israeli airstrike in Rafah, Gaza Strip, on March 4, 2024. An AP analysis of Gaza Health Ministry data finds the proportion of Palestinian women and children being killed in the Israel-Hamas war appears to have declined sharply. Israel faces heavy international criticism over unprecedented levels of civilian casualties in Gaza. (AP Photo/Fatima Shbair, File)

FILE - Palestinians pray over bodies of people killed in the Israeli bombardment of the Gaza Strip, on Nov. 22, 2023. An AP analysis of Gaza Health Ministry data finds the proportion of Palestinian women and children being killed in the Israel-Hamas war appears to have declined sharply. Israel faces heavy international criticism over unprecedented levels of civilian casualties in Gaza. (AP Photo/Mohammed Dahman, File)

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JERUSALEM (AP) — The Israel-Hamas war appears to have become much less deadly for Palestinian women and children, according to an AP analysis of Gaza Health Ministry data.

The shift is significant because the death rate for women and children is the best available proxy for civilian casualties in one of the 21st century’s most destructive conflicts .

Women and children made up fewer than 40% of those killed in the Gaza Strip during April, down from more than 60% in October. The decline both coincides with Israel’s changing battlefield tactics and contradicts the ministry’s own public statements.

Here are takeaways from The Associated Press’ reporting.

FATALITY TRENDS AND THE TACTICS OF WAR

After Hamas’ Oct. 7 attack, Israel launched an intense aerial bombardment on densely populated Gaza, and then invaded with thousands of ground troops backed by tanks and artillery.

By the end of October women and people 17 and younger accounted for 64% of the 6,745 killed who were fully identified by the Health Ministry.

After saying it had achieved many key objectives, the Israeli army began withdrawing ground troops earlier this year. It has focused lately on drone strikes and limited ground operations .

This is a locator map for Yemen with its capital, Sanaa. (AP Photo)

As the intensity of fighting has scaled back, the death toll has continued to rise, but at a slower rate – and with seemingly fewer civilians caught in the crossfire. During the month of April, women and children made up 38% of the fully identified deaths, the Health Ministry’s most recent data shows.

A TALE OF TWO DEATH TOLLS

The ministry announces a new death toll for the war nearly every day. It also has periodically released the underlying data behind this figure, including detailed lists of the dead.

The AP’s analysis looked at these lists, which were shared on social media in late October, early January, late March, and the end of April.

As recently as March, the ministry claimed over several days that 72% of the dead were women and children, even as underlying data showed the percentage was well below that.

Israeli leaders have pointed to such inconsistencies as evidence that the ministry is inflating the figures for political gain.

Experts say the reality is more complicated and that the ministry has been overwhelmed by war, making it difficult to track casualties.

CIVILIAN DEATHS FUEL CRITICISM OF ISRAEL

The true toll in Gaza could have serious repercussions.

Israel faces heavy international criticism over unprecedented levels of civilian casualties in Gaza and questions about whether it has done enough to prevent them in an eight-month-old war that shows no sign of ending. An airstrike in Rafah last month killed dozens of Palestinians, and one on a school-turned-shelter in central Gaza on Thursday killed at least 33 people, including 12 women and children , health officials said.

Two international courts in the Hague are examining accusations that Israel has committed war crimes and genocide against Palestinians – allegations it adamantly denies.

Israel says it has tried to avoid civilian casualties, issuing mass evacuation orders ahead of intense military operations that have displaced some 80% of Gaza’s population. It also accuses Hamas of intentionally putting civilians in harm’s way as human shields.

The fate of women and children is an important indicator of civilian casualties because the Health Ministry does not break out combatant deaths. But it’s not a perfect indicator: Many civilian men have died, and some older teenagers may be involved in the fighting.

MANY DEATHS COUNTED IN GAZA REMAIN ‘UNIDENTIFIED’

The ministry said publicly on April 30 that 34,622 had died in the war. The AP analysis was based on the 22,961 individuals fully identified at the time by the Health Ministry with names, genders, ages, and Israeli-issued identification numbers.

The ministry says 9,940 of the dead – 29% of its April 30 total – were not listed in the data because they remain “unidentified.” These include bodies not claimed by families, decomposed beyond recognition or whose records were lost in Israeli raids on hospitals.

An additional 1,699 records in the ministry’s April data were incomplete and 22 were duplicates; they were excluded from AP’s analysis.

Among those fully identified, the records show a steady decline in the overall proportion of women and children who have been killed: from 64% in late October, to 62% as of early January, to 57% at the end of March, to 54% at the end of April.

Some critics say the ministry’s imprecise methodologies – relying on families and “media reports” to confirm deaths – have added additional uncertainty to the figures.

The Health Ministry says it has gone to great lengths to accurately compile information but that its ability to count and identify the dead has been hampered by the war.

HEALTH MINISTRY STANDS BY ITS COUNT

Dr. Moatasem Salah, director of the ministry’s emergency center, rejected Israeli assertions that his ministry has intentionally inflated or manipulated the death toll.

“This shows disrespect to the humanity for any person who exists here,” he said. “We are not numbers … These are all human souls.”

He insisted that 70% of those killed have been women and children and said the overall death toll is much higher than what has been reported because thousands of people remain missing or are believed to be buried in rubble.

Israel last month angrily criticized the U.N.’s use of data from Hamas’ media office – a propaganda arm of the militant group – that reported a larger number of women and children killed. The U.N. later lowered its number in line with Health Ministry figures.

The number of Hamas militants killed in the fighting is also unclear. Hamas has closely guarded this information, though Khalil al-Hayya, a top Hamas official, told the AP in late April that the group had lost no more than 20% of its fighters. That would amount to roughly 6,000 fighters based on Israeli pre-war estimates.

The Israeli military has not challenged the overall death toll released by the Palestinian ministry. But it says the number of dead militants is much higher at roughly 15,000 – or over 40% of all the dead. It has provided no evidence to support the claim, and declined to comment for this story.

Michael Spagat, a London-based economics professor who chairs the board of Every Casualty Counts, a nonprofit that tracks armed conflicts, said he continues to trust the Health Ministry and believes it is doing its best in difficult circumstances.

“I think (the data) becomes increasingly flawed,” he said. But, he added, “the flaws don’t necessarily change the overall picture.”

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9 Questions to Help You Figure Out Why You’re Burned Out

Rebecca Zucker

It’s likely a combination of factors.

The World Health Organization characterizes burnout as comprising three key dimensions: sustained feelings of exhaustion, feelings of personal inefficacy, and increased mental distance from one’s job. In this article, the author outlines nine questions to ask yourself under each of these three categories to help you diagnose what’s causing your burnout. It’s likely a combination of factors, requiring a number of changes over time to fully address it, and not something a one-off vacation can reverse right away. Nonetheless, the answers to these questions serve as a starting point and can inform steps you can take to address your burnout and possibly prevent it from happening again in the future.

It’s no secret that managers and employees have been suffering from burnout for quite some time.

Rebecca Zucker is an executive coach and a founding partner at Next Step Partners , a leadership development firm. Her clients have included Amazon, Clorox, Morrison Foerster, Norwest Venture Partners, The James Irvine Foundation, and high-growth technology companies like DocuSign and Dropbox. You can follow her on LinkedIn .

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U.S. Uninsured Rate Drops by 26% Since 2019

A line graph showing the declining trend for uninsured adults aged 18–64 from 2019 through 2022.

The findings are featured in the report, “ Health Insurance Coverage: Early Release of Estimates from the National Health Interview Survey, 2023 .” It shows that among working-age Americans (those ages 18–64), 10.9% did not have health insurance in 2023, a decrease from 14.7% in 2019.

Highlights from the report include :

6% or 25.0 million Americans of all ages did not have health insurance in 2023 compared to 10.3% or 33.2 million in 2019.
In 2023, 3.9% or 2.8 million children did not have health insurance compared with 5.1% or 3.7 million in 2019.
Almost two-thirds (64.4%) of people younger than 65 were covered by private health insurance and more than a quarter (28.6%) were covered by public health insurance in 2023.
Among White, non-Hispanic adults ages 18–64, the percentage who were uninsured decreased by 35% from 10.5% in 2019 to 6.8% in 2023.
In 2023, almost 1 in 4 Hispanic adults ages 18–64 (24.8%) lacked health insurance, a greater percentage than Black, non-Hispanic adults (10.4%), White, non-Hispanic adults (6.8%) and Asian, non-Hispanic adults (4.4%).
The percentage of Americans younger than 65 with exchange-based private health insurance increased from 3.7% in 2019 to 4.8% in 2023.

Due to changes in various methodological aspects of the NHIS, this report only presents trends starting with 2019. Direct comparisons between estimates prior to 2019 should be made with caution.

The report is available on the NCHS web site at www.cdc.gov/nchs .

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Statistics & B.Maths, BCOM Part 1, Past Papers (2021)
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Ace Your Statistics Exam: Question 4 Properties of the Mean
An Interview with a Statistician
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STATISTICS Concepts for Data Science pt.1

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50 Best Statistics Essay Topics [2024 Updated]
20 Statistics Essay Topics for 2024. The impact of artificial intelligence on statistical analysis and data interpretation. Analyzing the effectiveness of COVID-19 vaccination campaigns using statistical models. The role of big data analytics in personalized marketing strategies.
Statistics, essay questions for Exam 3 Flashcards
For each statistic, tell the combination of variables, the symbol, the type of relationship being tested for, and the appropriate graphical display. The three types of bivariate statistical tests are Chi-square, t-test/ANOVA and correlation. Chi-square, x2, is used when there are two qualitative statistics. Look for pattern of relationship.
Top 99+ Trending Statistics Research Topics for Students
If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-. Literacy rate in a city. Abortion and pregnancy rate in the USA. Eating disorders in the citizens.
Statistics
That is, if you find an essay that quotes a number of statistics in support of its argument, often the author of the essay is using someone else's data. Thus, you need to consider not only your source, but the author's sources as well. ... Match your facts with your questions. Be sure that your statistics actually apply to the point ...
Free Statistics Essay Examples & Topic Ideas
Check our 100% free statistics essay, research paper examples. Find inspiration and ideas Best topics Daily updates. IvyPanda® Free Essays. Clear. Study Hub. Study Blog. Expert Q&A. Literature Guides. ... Statistics Essay Examples and Topics. Updated: May 27th, 2024 363 samples
Statistics Past Papers & Questions by Topic
Make your way through our clear revision notes, Statistics past papers, typical exam questions, fully-explained model answers and more, tailored to your Statistics specification. Get started by selecting your qualification level, and dive into everything you'll need to make the grade. GCSE. O Level. AP.
Statistics Free Essay Examples And Topic Ideas
Writing a statistics essay involves more than just presenting numbers and data. It requires a clear understanding of statistical methods, an ability to interpret results, and the skill to communicate findings effectively. This article provides a step-by-step guide on how to write a compelling statistics essay. Understanding the Essay Question
Free Statistics Essay Examples, Topic Ideas, Outlines
Your essay writing should start with an introduction where you talk about what statistical data has been used and what objectives have been placed. These Statistics essays that you see below explore various methods of getting things done. Compare our Statistics essay samples with your instructions, seek similarities, follow the same rules ...
The Beginner's Guide to Statistical Analysis
Table of contents. Step 1: Write your hypotheses and plan your research design. Step 2: Collect data from a sample. Step 3: Summarize your data with descriptive statistics. Step 4: Test hypotheses or make estimates with inferential statistics.
Introductory essay
Statistics. Introductory essay. Written by the educators who created Visualizing Data, a brief look at the key facts, tough questions and big ideas in their field. Begin this TED Study with a fascinating read that gives context and clarity to the material.
Statistics Essays: Samples & Topics
The Affects of Demographic Statistics on Human Growth and Other Factors. Demography is the statistical sample of people, particularly in terms of size and density, distribution and vital statistics such as births, marriages, deaths, etc. History has its own ups and downs forever, and demography has been helping us since forever to study the ...
Statistics Essay Topics to Write an Outstanding & Flawless Academic
Using our specific statistics essay topics for inspiration, you may come up with a brand-new issue to discuss in your essay or choose one from the list and work on it from A to Z: The Results of the Collaborative Work Between Egon Pearson and Jerzy Neyman. Importance of Statistics and Mathematics to Economics.
PDF Practice Exam Questions; Statistics 301; Professor Wardrop
3. A sample of size 40 yields the following sorted data. Note that I have x-ed outx (39) (the sec- ond largest number). This fact will NOT pre-vent you from answering the questions below.
Statistic
Statistics is the branch of mathematics that deals with collecting, analyzing, interpreting, and presenting data. It involves the use of methods to organize and summarize large amounts of information, draw conclusions from data, and make informed decisions based on the results. Statistics can help us understand trends and patterns in data ...
Statistics Essay Questions
Below is a list of sample essay questions in the subject of Statistics. Get Essay Help from the experts! Requesting statistical analysis using SPSS software and utilising several techniques. We have a range of statistics resources to help you with your studies: Statistics Essay Writing Service Free Statistics Essays.
Statistics Questions (Examples of Statistics Questions and Answers)
Questions on Statistics with Answers. 1. Give any two examples of collecting data from day-to-day life. Solution: A. Increase in population of our country in the last two decades. B. Number of tables and chairs in a classroom. Presentation of Data: After the collection of data, when we represent them in the form of table or chart or any other ...
Statistics Essay
By the 18th century, the term "statistics" designated the systematic collection ofdemographic and economic data by states. In the early 19th century, the meaning of "statistics" broadened to include the discipline concerned with the collection, summary, and analysis of data. Today statistics is widely employed in government. 1100 Words. 5 Pages.
Download and practice with free sample questions
See sample questions to help you practice for ACCUPLACER ESL tests. PDF. 212.16 KB. Download sample essay prompts and view scored essays. WritePlacer and WritePlacer ESL guides show you the types of prompts you might see on the WritePlacer tests, as well as examples of responses to those prompts, the scores they received, and why they received ...
Essay On Statistics Questions
Question 1. a. When a researcher studies a process or a phenomenon, it is impossible to involve the whole population in the analysis, therefore the certain assumptions and limitations are necessary. Supposing, the population has the mean μ and the standard deviation σ, and the quantity N. The sampling distribution is the smaller sample with ...
Statistics
Welcome. Please use the tabs to search for Books, Articles and Web Resources. If you have any questions, do not hesitate to contact the Skyline College Librarians. Related research guides include: o California Statistics & Issues.
Essay on Statistics Questions
QUESTION 21 1. On average, how many blemishes do we expect on one piece of new furniture? 2. What is the variance of blemishes on one piece of new furniture? (round to the nearest hundredth) QUESTION 22 p = 0.4 q = 1-p = 1-0.4 = 0.6 n = 10. 1) Mean = np = 10*0.4 = 4.
Questions and Answers on Statistics
Satisfactory Essays. 2449 Words. 10 Pages. Open Document. Answering Eight Questions Distinguish common variation and special variation. Provide specific examples The common cause variation and special cause variation are two original methods of variation process in statistical analysis. Common cause variation Variation is a necessary facet in ...
IELTS Statistics
16. Problem-and-Solution Essay. 8. IELTS essay type distribution is more smooth; however, the major type — Argument Essay — also keeps a hefty number (roughly 50%) of all Writing Task 2 questions. The second most frequent type, Opinion Essay, is half as common. The Two-question Essay type represents about 15% of the pie.
Welcome to the Purdue Online Writing Lab
The Online Writing Lab (the Purdue OWL) at Purdue University houses writing resources and instructional material, and we provide these as a free service at Purdue.
Analyzing Roberto Clemente's Stellar Baseball Career through his Statistics
Essay Example: Roberto Clemente, a name forever etched in the rich history of baseball, stands as a beacon of excellence and resilience. His illustrious career, a tapestry of unparalleled achievements and unforgettable milestones, continues to inspire athletes and fans across generations. To
Top Satire Topics for Essays: Engaging and Funny Satire Ideas by
60 Satire Essay Topics for Students Good Satire Topics. The Social Media Influencer Phenomenon: Satirize the rise of influencers and their impact on society, focusing on the absurdity of their influence on lifestyle and consumer habits.; Political Campaign Promises: Highlight the empty promises made during political campaigns and the gullibility of voters who believe them.
Questions and Answers for Applied Statistics and Multilevel Modeling
Questions and Answers for Applied Statistics and Multilevel Modeling Posted on June 11, 2024 9:26 AM by Andrew Last semester, every student taking my course was required to contribute before each class to a shared Google doc by putting in a question about the reading or the homework, or by answering another student's question.
Takeaways from AP analysis of Gaza Health Ministry's death toll data
2 of 2 | . FILE - Palestinians pray over bodies of people killed in the Israeli bombardment of the Gaza Strip, on Nov. 22, 2023. An AP analysis of Gaza Health Ministry data finds the proportion of Palestinian women and children being killed in the Israel-Hamas war appears to have declined sharply.
9 Questions to Help You Figure Out Why You're Burned Out
The World Health Organization characterizes burnout as comprising three key dimensions: sustained feelings of exhaustion, feelings of personal inefficacy, and increased mental distance from one ...
U.S. Uninsured Rate Drops by 26% Since 2019
CDC - Blogs - NCHS: A Blog of the National Center for Health Statistics - U.S. Uninsured Rate Drops by 26% Since 2019 - Featured Topics from the National Center for Health Statistics Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link