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Introduction to Statistical Hypothesis Testing in Nursing Research

Keeler, Courtney PhD, RN; Curtis, Alexa Colgrove PhD, FNP, PMHNP

Courtney Keeler is an associate professor and Alexa Colgrove Curtis is assistant dean of graduate nursing and director of the MPH–DNP dual degree program, both at the University of San Francisco School of Nursing and Health Professions. Contact author: Courtney Keeler, [email protected] . Bernadette Capili, PhD, NP-C, is the column coordinator: [email protected] . This manuscript was supported in part by grant No. UL1TR001866 from the National Institutes of Health's National Center for Advancing Translational Sciences Clinical and Translational Science Awards Program. The authors have disclosed no potential conflicts of interest, financial or otherwise.

Editor's note: This is the 16th article in a series on clinical research by nurses. The series is designed to be used as a resource for nurses to understand the concepts and principles essential to research. Each column will present the concepts that underpin evidence-based practice—from research design to data interpretation. To see all the articles in the series, go to https://links.lww.com/AJN/A204 .

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Measurement in nursing research, descriptive and inferential statistics in nursing research, sample size planning in quantitative nursing research, selection of the study participants, sampling design in nursing research.

The P value: What it really means

As nurses, we must administer nursing care based on the best available scientific evidence. But for many nurses, critical appraisal, the process used to determine the best available evidence, can seem intimidating. To make critical appraisal more approachable, let’s examine the P value and make sure we know what it is and what it isn’t.

Defining P value

The P value is the probability that the results of a study are caused by chance alone. To better understand this definition, consider the role of chance.

The concept of chance is illustrated with every flip of a coin. The true probability of obtaining heads in any single flip is 0.5, meaning that heads would come up in half of the flips and tails would come up in half of the flips. But if you were to flip a coin 10 times, you likely would not obtain heads five times and tails five times. You’d be more likely to see a seven-to-three split or a six-to-four split. Chance is responsible for this variation in results.

Just as chance plays a role in determining the flip of a coin, it plays a role in the sampling of a population for a scientific study. When subjects are selected, chance may produce an unequal distribution of a characteristic that can affect the outcome of the study. Statistical inquiry and the P value are designed to help us determine just how large a role chance plays in study results. We begin a study with the assumption that there will be no difference between the experimental and control groups. This assumption is called the null hypothesis. When the results of the study indicate that there is a difference, the P value helps us determine the likelihood that the difference is attributed to chance.

Competing hypotheses

In every study, researchers put forth two kinds of hypotheses: the research or alternative hypothesis and the null hypothesis. The research hypothesis reflects what the researchers hope to show—that there is a difference between the experimental group and the control group. The null hypothesis directly competes with the research hypothesis. It states that there is no difference between the experimental group and the control group.

It may seem logical that researchers would test the research hypothesis—that is, that they would test what they hope to prove. But the probability theory requires that they test the null hypothesis instead. To support the research hypothesis, the data must contradict the null hypothesis. By demonstrating a difference between the two groups, the data contradict the null hypothesis.

Testing the null hypothesis

Now that you know why we test the null hypothesis, let’s look at how we test the null hypothesis.

After formulating the null and research hypotheses, researchers decide on a test statistic they can use to determine whether to accept or reject the null hypothesis. They also propose a fixed-level P value. The fixed level P value is often set at .05 and serves as the value against which the test-generated P value must be compared. (See Why .05?)

A comparison of the two P values determines whether the null hypothesis is rejected or accepted. If the P value associated with the test statistic is less than the fixed-level P value, the null hypothesis is rejected because there’s a statistically significant difference between the two groups. If the P value associated with the test statistic is greater than the fixed-level P value, the null hypothesis is accepted because there’s no statistically significant difference between the groups.

The decision to use .05 as the threshold in testing the null hypothesis is completely arbitrary. The researchers credited with establishing this threshold warned against strictly adhering to it.

Remember that warning when appraising a study in which the test statistic is greater than .05. The savvy reader will consider other important measurements, including effect size, confidence intervals, and power analyses when deciding whether to accept or reject scientific findings that could influence nursing practice.

Real-world hypothesis testing

How does this play out in real life? Let’s assume that you and a nurse colleague are conducting a study to find out if patients who receive backrubs fall asleep faster than patients who do not receive backrubs.

1. State your null and research hypotheses

Your null hypothesis will be that there will be no difference in the average amount of time it takes patients in each group to fall asleep. Your research hypothesis will be that patients who receive backrubs fall asleep, on average, faster than those who do not receive backrubs. You will be testing the null hypothesis in hopes of supporting your research hypothesis.

2. Propose a fixed-level P value

Although you can choose any value as your fixed-level P value, you and your research colleague decide you’ll stay with the conventional .05. If you were testing a new medical product or a new drug, you would choose a much smaller P value (perhaps as small as .0001). That’s because you would want to be as sure as possible that any difference you see between groups is attributed to the new product or drug and not to chance. A fixed-level P value of .0001 would mean that the difference between the groups was attributed to chance only 1 time out of 10,000. For a study on backrubs, however, .05 seems appropriate.

3. Conduct hypothesis testing to calculate a probability value

You and your research colleague agree that a randomized controlled study will help you best achieve your research goals, and you design the process accordingly. After consenting to participate in the study, patients are randomized to one of two groups:

• the experimental group that receives the intervention—the backrub group
• the control group—the non-backrub group.

After several nights of measuring the number of minutes it takes each participant to fall asleep, you and your research colleague find that on average, the backrub group takes 19 minutes to fall asleep and the non-backrub group takes 24 minutes to fall asleep.

Now the question is: Would you have the same results if you conducted the study using two different groups of people? That is, what role did chance play in helping the backrub group fall asleep 5 minutes faster than the non-backrub group? To answer this, you and your colleague will use an independent samples t-test to calculate a probability value.

An independent samples t-test is a kind of hypothesis test that compares the mean values of two groups (backrub and non-backrub) on a given variable (time to fall asleep).

Hypothesis testing is really nothing more than testing the null hypothesis. In this case, the null hypothesis is that the amount of time needed to fall asleep is the same for the experimental group and the control group. The hypothesis test addresses this question: If there’s really no difference between the groups, what is the probability of observing a difference of 5 minutes or more, say 10 minutes or 15 minutes?

We can define the P value as the probability that the observed time difference resulted from chance. Some find it easier to understand the P value when they think of it in relationship to error. In this case, the P value is defined as the probability of committing a Type 1 error. (Type 1 error occurs when a true null hypothesis is incorrectly rejected.)

4. Compare and interpret the P value

Early on in your study, you and your colleague selected a fixed-level P value of .05, meaning that you were willing to accept that 5% of the time, your results might be caused by chance. Also, you used an independent samples t-test to arrive at a probability value that will help you determine the role chance played in obtaining your results. Let’s assume, for the sake of this example, that the probability value generated by the independent samples t-test is .01 (P = .01). Because this P value associated with the test statistic is less than the fixed-level statistic (.01 < .05), you can reject the null hypothesis. By doing so, you declare that there is a statistically significant difference between the experimental and control groups. (See Putting the P value in context.)

In effect, you’re saying that the chance of observing a difference of 5 minutes or more, when in fact there is no difference, is less than 5 in 100. If the P value associated with the test statistic would have been greater than .05, then you would accept the null hypothesis, which would mean that there is no statistically significant difference between the control and experimental groups. Accepting the null hypothesis would mean that a difference of 5 minutes or more between the two groups would occur more than 5 times in 100.

Putting the P value in context

Although the P value helps you interpret study results, keep in mind that many factors can influence the P value—and your decision to accept or reject the null hypothesis. These factors include the following:

• Insufficient power. The study may not have been designed appropriately to detect an effect of the independent variable on the dependent variable. Therefore, a change may have occurred without your knowing it, causing you to incorrectly reject your hypothesis.
• Unreliable measures. Instruments that don’t meet consistency or reliability standards may have been used to measure a particular phenomenon.
• Threats to internal validity. Various biases, such as selection of patients, regression, history, and testing bias, may unduly influence study outcomes.

A decision to accept or reject study findings should focus not only on P value but also on other metrics including the following:

• Confidence intervals (an estimated range of values with a high probability of including the true population value of a given parameter)
• Effect size (a value that measures the magnitude of a treatment effect)

Remember, P value tells you only whether a difference exists between groups. It doesn’t tell you the magnitude of the difference.

5. Communicate your findings

The final step in hypothesis testing is communicating your findings. When sharing research findings (hypotheses) in writing or discussion, understand that they are statements of relationships or differences in populations. Your findings are not proved or disproved. Scientific findings are always subject to change. But each study leads to better understanding and, ideally, better outcomes for patients.

Key concepts

The P value isn’t the only concept you need to understand to analyze research findings. But it is a very important one. And chances are that understanding the P value will make it easier to understand other key analytical concepts.

Selected references

Burns N, Grove S: The Practice of Nursing Research: Conduct, Critique, and Utilization. 5th ed. Philadelphia: WB Saunders; 2004.

Glaser DN: The controversy of significance testing: misconceptions and alternatives. Am J Crit Care. 1999;8(5):291-296.

Kenneth J. Rempher, PhD, RN, MBA, CCRN, APRN,BC, is Director, Professional Nursing Practice at Sinai Hospital of Baltimore (Md.). Kathleen Urquico, BSN, RN, is a Direct Care Nurse in the Rubin Institute of Advanced Orthopedics at Sinai Hospital of Baltimore.

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• Volume 18, Issue 1
• Which statistical tests should I use?
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• Allison Shorten 1 ,
• Brett Shorten 2
• 1 Yale School of Nursing, Yale University , New Haven, Connecticut , USA
• 2 Informed Health Choices Trust , New South Wales , Australia
• Correspondence to : Dr Allison Shorten , Yale School of Nursing, Yale University, Yale West Campus, 400 West Campus Drive, Orange, CT 06477, USA; allison.shorten{at}yale.edu

https://doi.org/10.1136/eb-2014-102003

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Statistical tests can be powerful tools for researchers. They provide valuable evidence from which we make decisions about the significance or robustness of research findings. Statistical tests are a critical part of the answers to our research questions and ultimately determine how confident we can be in the evidence to inform clinical practice. In addition to analysing data to answer research questions, readers of research also need to understand the underlying principles of common statistical tests. It is helpful to know whether statistical tests have been applied in the right way, at the right time with the right data.

There are dozens of statistical tests for researchers to choose from. The statistical tests we use help us gather evidence on which we will either accept or reject our stated null hypotheses 1 and therefore make conclusions about the findings of an experiment or exploration. The best way for researchers to ensure they are using the right statistical tests is to consult a specialist in statistics when research is being planned and before data is collected. Mapping out the analysis is an important step in research planning. Statistical tests should not be used as a substitute for good research design or to attempt to correct serious flaws in data.

We will use a hypothetical example to outline some common statistical tests we could use to answer common research questions. Let's say we developed a new ‘mobile phone app’ for patients recently diagnosed with diabetes, with the aim improving patient knowledge about healthy food choices. A sample of patients attending a diabetic clinic was carefully selected according to sound principles 2 and 200 patients were randomised into two groups. One group (n=100) would use the ‘mobile phone app’ over a period of 3 months and the other group would be the control (n=100), receiving their usual care in the clinic. Let's say we also developed a 15-item knowledge test consisting of true/false questions, based on the content in the ‘mobile app’ to assess levels of knowledge about healthy food choices. We administered this test to all patients both before and after the 3-month testing period. Now we will explore how different statistical tests can help us as we answer some key research questions that could have been asked by the researchers as they were planning the study. Note that different texts and statistical software packages sometimes use slightly different names for a given statistical test.

Question 1 : Did patients have any baseline knowledge about healthy food choices before the study started?

What can we test? Suppose we wish to test whether the patients had any baseline knowledge of healthy food choices. If not, we would expect them, on average, to score 7.5 out of 15 on a true/false test (ie, 50%) even if they were guessing. So, for this test, the null hypothesis is that, for the population of all patients, baseline knowledge is 0 (mean=7.5) and the alternative is that patients already have some knowledge regarding healthy eating (mean >7.5).

 Appropriate test : one sample t test.

Purpose of the test : this test is used to compare a single sample mean with a hypothesised population value.

What the results might look like? If the mean score of all 200 patients was higher than 7.5 with associated p value <0.05, then there is evidence that patients had at least some background knowledge of healthy eating.

Question 2 : Did patients using the ‘mobile app’ score better on their knowledge test than those receiving usual care?

What can we test? We can test the hypothesis that mean knowledge scores were different between the patients who used the ‘mobile app’ and those receiving usual care, against the null hypothesis that mean scores were the same for both groups of patients.

 Appropriate test : independent samples t test.

Purpose of the test : to compare means from two samples, to test whether they are significantly different or could plausibly have come from populations with the same mean value.

What the results might look like? If 200 patients completed the knowledge test after 3 months and the mean scores out of 15 were 11.30 for the ‘mobile app’ group and 9.05 for the control group and if the t statistic result was, say, 6.48 and associated p value was 0.000, this indicates that the difference observed between sample means was too large to be simply due to chance. We can be confident that the knowledge scores for patients in the ‘mobile app’ group were statistically greater than for the control group. A reported 95% CI of 1.57 to 2.93 for this test would indicate that it is 95% likely that the true population difference in mean knowledge scores in favour of the ‘mobile app’ group is between 1.57 and 2.93 questions.

Question 3 : Did patients without the “mobile app” experience any improvements in knowledge over time?

What can we test? We can test the claim that, even without the ‘mobile app’ patient knowledge of healthy food choices improved over the period of 3 months, perhaps due to the effects of their routine clinic care. We can create a new variable defined as (postintervention score minus preintervention score). For example, a patient in the control group who scored 10 on the quiz at the start of the study and 12 at the end of 3 months would have the value of 12−10=2 for this new variable.

 Appropriate test : paired samples t test.

Purpose of the test : to compare two sample means where the data represent two observations of the same variable from each respondent (typically before and after scores or measurements).

What might the results look like? Suppose the mean difference in knowledge scores for the 100 patients who did not have the ‘mobile app’ was 0.43, with a calculated t statistic of 2.15 and p value of 0.034. This would indicate that the difference was statistically significant at the conventional significance level of 0.05. Therefore we would be confident that there was some improvement in knowledge among patients receiving usual care. If the reported 95% CI for this test was 0.03 to 0.82, it would suggest that we can be 95% confident that, in the overall population, the increase in correct responses would be in the range of 0.03–0.82 questions, or less than one question on the test.

Question 4 : Did the education level of patients have an influence on patient preintervention level of knowledge (ie, before they started using the ‘mobile app’)?

What can we test? We could use this test if we wanted to examine the effect of level of education on preintervention knowledge scores, where education was measured in three categories: secondary school, diploma/certificate and bachelor degree or higher.

 Appropriate test : analysis of variance .

Purpose of the test : to compare three or more means, in order to test whether some or all means are significantly different or whether all could plausibly have come from populations with the same mean value.

What might the results look like? Suppose the mean preintervention knowledge scores out of 15 for the three groups according to the level of education were 7.74, 8.70 and 9.56 respectively; the calculated F statistic was 9.83, with an associated p value of 0.000. This would mean there is convincing evidence that mean knowledge scores vary by level of education. Note that this does not mean that all three means are significantly different from each other. A range of further tests are available in this area. For example, another popular test called the Tukey test could be used to compare mean scores to see if, for example, secondary and degree or higher (7.74 vs 9.56) scores were statistically different.

Question 5 : Could other factors have had a confounding effect on the differences observed in levels of knowledge for the patients?

What can we test? Suppose we are concerned that what we think has been a positive effect of the ‘mobile app’ has actually been confounded by unmeasured differences between the two groups involving English literacy levels, due to some patients being born overseas. We could use linear regression techniques to control for possible confounding factors such as whether patients have non-English speaking background. This way, after controlling for patients’ history, we could better assess whether the improvements in knowledge were due to the ‘mobile app’.

 Appropriate test : linear multiple regression analysis.

Purpose of the test : regression analysis is a large and often complex field in its own right, with many different uses. In this type of study we might use regression models to allow us to control for a range of confounding factors that may affect the validity of the results obtained.

What the results might look like? If we used a linear multiple regression model and found that the regression estimate (known as a coefficient) was +2.29 it would tell us that, after controlling for place of birth, patients who received the ‘mobile app’ were estimated to get 2.29 more questions correct, on average, than patients in the control group. Note that it is common for a larger range of possible confounders to be included in a linear multiple regression model and this is just an example of one.

Remember, consult an expert to help select the right statistical tests for your research questions and gather the best evidence to inform future clinical practice.

• Shorten A ,

Competing interests None.

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Hypothesis testing: selection and use of statistical tests

20 Hypothesis testing selection and use of statistical tests Chapter Contents Introduction  The logic of hypothesis testing  Steps in hypothesis testing  Illustrations of hypothesis testing  The relationship between descriptive and inferential statistics  Selection of the appropriate inferential test  The χ 2 test  χ 2 and contingency tables  Statistical packages  Summary  Introduction Hypotheses are statements about the association between variables as pertaining to a specific person or population. For example, ‘penicillin is an effective treatment for pneumonia’ or ‘obesity is a risk factor for heart disease’. Hypotheses addressing the state of populations are tested using sample data. Inferences are conclusions based on data using samples and are therefore always open to the possibility of error. In this chapter we will examine the use of inferential statistics for establishing the probable truth of hypotheses, as tested through sample data. Inferential statistics are based on applied probability theory and entail the use of statistical tests. There are numerous statistical tests available that are used in a similar fashion to analyse clinical data. That is, all statistical tests involve setting up the relevant hypotheses, H 0 and H A , and then, on the basis of the appropriate inferential statistics, computing the probability of the sample statistics obtained occurring by chance alone. We are not going to attempt to examine all statistical tests in this introductory book. These are described in various statistics textbooks or in data analysis manuals. Rather, in this chapter we will examine the criteria used for selecting tests appropriate for the analysis of the data obtained in specific investigations. To illustrate the use of statistical tests we will examine the use of the chi-square test (χ 2 ). This is a statistical test commonly employed to analyse categorical data. Finally, we will briefly examine the uses of the Statistical Package for Social Sciences™ (SPSS) for data analysis in general. The aims of this chapter are to: 1.  Discuss the criteria by which a statistical test is selected for analysing the data for a specific study. 2.  Demonstrate the use of the χ 2 test for analysing nominal scale data. 3.  Explain how statistical packages are used for quantitative data analysis. The logic of hypothesis testing Hypothesis testing is the process of deciding using statistics whether the findings of an investigation reflect chance or real effects at a given level of probability or certainty. If the results seem to not represent chance effects, then we say that the results are statistically significant. That is, when we say that our results are statistically significant we mean that the patterns or differences seen in the sample data are likely to be generalizable to the wider population from our study sample. The mathematical procedures for hypothesis testing are based on the application of probability theory and sampling, as discussed previously. Because of the probabilistic nature of the process, decision errors in hypothesis testing cannot be entirely eliminated. However, the procedures outlined in this chapter enable us to specify the probability level at which we can claim that the data obtained in an investigation support experimental hypotheses. This procedure is fundamental for determining the statistical significance of the data as well as being relevant to the logic of clinical decision making. Steps in hypothesis testing The following steps are conventionally followed in hypothesis testing: 1.  State the alternative hypothesis (H A ), which is the based on the research hypothesis. The H A asserts that the results are ‘real’ or ‘significant’, i.e. that the independent variable influenced the dependent variable, or that there is a real difference among groups. The important point here is that H A is a statement concerning the population. A real or significant effect means that the results in the sample data can be generalized to the population. 2.  State the null hypothesis (H 0 ), which is the logical opposite of the H A . The H 0 claims that any differences in the data were just due to chance: that the independent variable had no effect on the dependent variable, or that any difference among groups is due to random effects. In other words, if the H 0 is retained, differences or patterns seen in the sample data should not be generalized to the population. 3.  Set the decision level, α (alpha). There are two mutually exclusive hypotheses (H A and H 0 ) competing to explain the results of an investigation. Hypothesis testing, or statistical decision making, involves establishing the probability of H 0 being true. If this probability is very small, we are in a position to reject the H 0 . You might ask ‘How small should the probability (α) be for rejecting H 0 ?’ By convention, we use the probability of α = 0.05. If the H 0 being true is less than 0.05, we can reject H 0 . We can choose an α of 0.05, but not more, That is, by convention among researchers, results are not characterized as significant if p > 0.05. 4.  Calculate the probability of H 0 being true. That is, we assume that H 0 is true and calculate the probability of the outcome of the investigation being due to chance alone, i.e. due to random effects. We must use an appropriate sampling distribution for this calculation. 5.  Make a decision concerning H 0 . The following decision rule is used. If the probability of H 0 being true is less than α, then we reject H 0 at the level of significance set by α. However, if the probability of H 0 is greater than α, then we must retain H 0 . In other words, if: a.  p (H 0 is true) ≤ α, reject H 0 b.  p (H 0 is true) > α, retain H 0 It follows that if we reject H 0 we are in a position to accept H A , its logical alternative. If p ≤ 0.05 then we reject H 0 , and decide that H A is probably true. Illustrations of hypothesis testing One of the simplest forms of gambling is betting on the fall of a coin. Let us play a little game. We, the authors, will toss a coin. If it comes out heads (H) you will give us ≤1; if it comes out tails (T) we will give you ≤1. To make things interesting, let us have 10 tosses. The results are: Oh dear, you seem to have lost. Never mind, we were just lucky, so send along your cheque for ≤10. Are you a little hesitant? Are you saying that we ‘fixed’ the game? There is a systematic procedure for demonstrating the probable truth of your allegations: 1.  We can state two competing hypotheses concerning the outcome of the game: a.  the authors fixed the game; that is, the outcome did not reflect the fair throwing of a coin. Let us call this statement the ‘alternative hypothesis’, H A . In effect, the H A claims that the sample of 10 heads came from a population other than P (probability of heads) = Q (probability of tails) = 0.5 b.  the authors did not fix the game; that is, the outcome is due to the tossing of a fair coin. Let us call this statement the ‘null hypothesis’, or H 0 . H 0 suggests that the sample of 10 heads was a random sample from a population where P = Q = 0.5. 2.  It can be shown that the probability of tossing 10 consecutive heads with a fair coin is actually p = 0.001, as discussed previously (see Ch. 19). That is, the probability of obtaining such a sample from a population where P = Q = 0.5 is extremely low. 3.  Now we can decide between H 0 and H A . It was shown that the probability of H 0 being true was p = 0.001 (1 in a 1000). Therefore, in the balance of probabilities, we can reject it as being true and accept H A , which is the logical alternative. In other words, it is likely that the game was fixed and no ≤10 cheque needed to be posted. The probability of calculating the truth of H 0 depended on the number of tosses ( n = the sample size). For instance, the probability of obtaining heads every times with five coin tosses is shown in Table 19.4 . As the sample size ( n ) becomes larger, the probability for which it is possible to reject H 0 becomes smaller. With only a few tosses we really cannot be sure if the game is fixed or not: without sufficient information it becomes hard to reject H 0 at a reasonable level of probability. A question emerges: ‘What is a reasonable level of probability for rejecting H 0 ?’ As we shall see, there are conventions for specifying these probabilities. One way to proceed, however, is to set the appropriate probability for rejecting H 0 on the basis of the implications of erroneous decisions. Obviously, any decision made on a probabilistic basis might be in error. Two types of decision errors are identified in statistics as type I and type II errors . A type I error involves mistakenly rejecting H 0 , while a type II error involves mistakenly retaining the H 0 . Researchers can make mistakes about the truth or falsity of hypotheses using sample research data. Statistical method does not provide a guarantee against making a mistake, but it is the most rigorous way of making these decisions. In the above example, a type I error would involve deciding that the outcome was not due to chance when in fact it was. The practical outcome of this would be to falsely accuse the authors of fixing the game. A type II error would represent the decision that the outcome was due to chance, when in fact it was due to a ‘fix’. The practical outcome of this would be to send your hard-earned ≤10 to a couple of crooks. Clearly, in a situation like this, a type II error would be more odious than a type I error, and you would set a fairly high probability for rejecting H 0 . However, if you were gambling with a villain, who had a loaded revolver handy, you would tend to set a very low probability for rejecting H 0 . We will examine these ideas more formally in subsequent parts of this chapter. Let us look at another example. A rehabilitation therapist has devised an exercise program which is expected to reduce the time taken for people to leave hospital following orthopaedic surgery. Previous records show that the recovery time for patients had been µ = 30 days, with σ = 8 days. A sample of 64 patients were treated with the exercise program, and their mean recovery time was found to be = 24 days. Do these results show that patients who had the treatment recovered significantly faster than previous patients? We can apply the steps for hypothesis testing to make our decision. 1.  State H A : ‘The exercise program reduces the time taken for patients to recover from orthopaedic surgery’. That is, the researcher claims that the independent variable (the treatment) has a ‘real’ or ‘generalizable’ effect on the dependent variable (time to recover). 2.  State H 0 : ‘The exercise program does not reduce the time taken for patients to recover from orthopaedic surgery’. That is, the statement claims that the independent variable has no effect on the dependent variable. The statement implies that the treated sample with = 24, and n = 64 is in fact a random sample from the population µ = 30, σ = 8. Any difference between and µ can be attributed to sampling error. 3.  The decision level, α, is set before the results are analysed. The probability of α depends on how certain the investigator wants to be that the results show real differences. If he set α = 0.01, then the probability of falsely rejecting a true H 0 is less than or equal to 0.01 (1/100). If he set α = 0.05, then the probability of falsely rejecting a true H 0 is less than or equal to 0.05 or (1/20). That is, the smaller the α, the more confident the researcher is that the results support the alternative hypothesis. We also call α the level of significance. The smaller the α, the more significant the findings for a study, if we can reject H 0 . In this case, say that the researcher sets α = 0.01. (Note: by convention, α should not be greater than 0.05.) 4.  Calculate the probability of H 0 being true. As stated above, H 0 implies that the sample with = 24 is a random sample from the population with µ = 30, σ = 8. How probable is it that this statement is true? To calculate this probability, we must generate an appropriate sampling distribution. As we have seen in Chapter 17 , the sampling distribution of the mean will enable us to calculate the probability of obtaining a sample mean of = 24 or more extreme from a population with known parameters. As shown in Figure 20.1 , we can calculate the probability of drawing a sample mean of = 24 or less. Using the table of normal curves (Appendix A), as outlined previously, we find that the probability of randomly selecting a sample mean of = 24 (or less) is extremely small. In terms of our table, which only shows the exact probability of up to z = 4.00, we can see that the present probability is less than 0.00003. Therefore, the probability that H 0 is true is less than 0.00003. Figure 20.1 Sampling distribution of means. Sample size = 64, population mean = 30, standard deviation = 8. 5.  Make a decision. We have set α = 0.01. The calculated probability was less than 0.0001. Clearly, the calculated probability is far less than α, indicating that the difference is unlikely to be due to chance. Therefore, the investigator can reject the statement that H 0 is true and accept H A , that patients in general treated with the exercise program recover earlier than the population of untreated patients. The relationship between descriptive and inferential statistics As we have seen in the previous chapters, statistics may be classified as descriptive or inferential. Descriptive statistics describe the characteristics of data and are concerned with issues such as ‘What is the average length of hospitalization of a group of patients?’ Inferential statistics are used to address issues such as whether the differences in average lengths of hospitalization of patients in two groups are significantly different statistically. Thus, descriptive statistics describe aspects of the data such as the frequencies of scores, and the average or the range of values for samples, whereas inferential statistics enables researchers to decide (infer) whether differences between groups or relationships between variables represent persistent and reproducible trends in the populations. In Section 5 we saw that the selection of appropriate descriptive statistics depends on the type of data being described. For example, in a variable such as incomes of patients, the best statistics to represent the typical income would be the mean and/or the median. If you had a millionaire in the group of patients, the mean statistic might give a distorted impression of the central tendency. In this situation the median statistic would be the most appropriate one to use. The mode is most commonly used when the data being described are categorical data. For example, if in a questionnaire respondents were asked to indicate their sex and 65% said they were male and 35% said they were female, then ‘male’ is the modal response. It is quite unusual to use the mode only with data that are not nominal. As a rule, the scale of measurement used to obtain the data and its distribution determine which descriptive statistics are selected. In the same way, the appropriate inferential statistics are determined by the characteristics of the data being analysed. For example, where the mean is the appropriate descriptive statistic, the inferential statistics will determine if the differences between the means are statistically significant. In the case of ordinal data, the appropriate inferential statistics will make it possible to decide if either the medians or the rank orders are significantly different. With nominal data, the appropriate inferential statistic will decide if proportions of cases falling into specific categories are significantly different. Thus, when the data have been adequately described, the appropriate inferential statistic will follow logically. However, when selecting an appropriate statistical test, the design of the investigation must also be taken into account.

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• Published: 27 September 2024

Relationship between hospital ethical climate, critical thinking disposition, and nursing task performance

• Seul-Ki Park   ORCID: orcid.org/0000-0003-0151-4161 1 &
• Yeo-Won Jeong   ORCID: orcid.org/0000-0003-3824-5209 2  

BMC Nursing volume  23 , Article number:  696 ( 2024 ) Cite this article

Metrics details

As ethical conflicts increase in the ever-changing healthcare field, nursing task performance, which is the overall ability of a nurse’s professional knowledge, attitude, and skills, is important for patient health and safety, the provision of quality nursing care, and the appropriate resolution of nursing ethical problems. This study aimed to evaluate the mediating effect of critical thinking disposition on the relationship between hospital ethical climate and nursing task performance.

A cross-sectional study was conducted. A total of a convenience sample of 200 clinical nurses from two Korean cities were recruited between November and December 2021. Direct questionnaires and online surveys were used to collect the data. The study variables were analyzed using descriptive statistics, correlations, and a model tested using the Hayes PROCESS macro (Model 4) mediation model.

The mean scores for hospital ethical climate, critical thinking disposition, and nursing task performance were 91.86 ± 11.29, 97.74 ± 10.70, and 138.58 ± 14.95, respectively. Hospital ethical climate and critical thinking disposition were positively correlated with nursing task performance. In the mediation test model, hospital ethical climate was found to be positively and significantly associated with nursing task performance (ß = 0.46, p  < .001) with the mediation of critical thinking disposition (ß = 0.70, p  < .001).

Conclusions

Hospital ethical climate and critical thinking disposition may be important determinants of task performance among clinical nurses. Hospital administrators should make efforts to create a more positive ethical climate in hospitals and conduct education and campaigns on a positive hospital ethical climate for hospital staff to improve nurses’ performance.

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Introduction

Owing to changes in disease structure and the rapidly aging population, nurses face ethical issues and ethically difficult situations in their daily work. The frequent triggers of ethical dilemmas in nursing practice are conflicting interpersonal relationships (patient, co-workers, or physicians), lack of trust of the patient or family member, workload affecting quality of nursing, poor organization of working process, and conflicts related to the health service and management system [ 1 , 2 ]. Haahr et al. reported that balancing harm and care is one of the ethical dilemmas, that refers to nurses’ values practice conflict leading them to perform nursing actions that are against their personal and professional values [ 1 ]. Moreover, the complexity of ethical situations in healthcare environments and nursing is increasing, affecting not only nurses but also the quality of nursing [ 3 , 4 ]. The Korean nursing community has adopted the Nursing Code of Ethics [ 5 ], emphasizing the significance of ethical environments and nurses’ ethical behavior in the field of nursing [ 6 ]. Ethical behavior of organizational members occurs among individuals; however, an individual’s unethical behavior may be condoned or aided depending on the organization’s ethical environment [ 6 ]. Therefore, the ethical climate of an organization influences the behavior or practice of members in the work environment.

Hospital ethical climate and nursing task performance

Hospital ethical climate refers to nurses’ perceptions of how ethical issues are handled in their work setting [ 7 , 8 ]. Different researchers mention multiple constructs of hospital ethical climate [ 7 , 9 ]; among them, our study adopted Olson’s conceptualization of hospital ethical climate with five dimensions related to colleagues, patients, managers, hospital/organization, and physicians [ 7 ]. Many studies on hospital ethical climate have focused on its association with work-related factors, such as job satisfaction, moral distress, and turnover intentions [ 4 , 6 ]. In South Korea, the variables commonly found in jobs and organizations are job stress, supervisor trust, and organizational commitment rather than personal [ 6 ]. In particular, the hospital ethical climate reflects organizational practices and values in care issues and is an important factor that affects the professional performance and ethical practices of nurses in that organization [ 8 ]. However, most previous studies did not address nursing professional practices as a personal variable, such as nursing task performance, when investigating hospital ethical climate [ 4 , 6 , 10 , 11 , 12 ]. In addition, Noh and Lee suggested expanding the research to evaluate the relationship between hospital ethical climate and various variables such as nursing tasks and nursing outcomes [ 6 ].

Nursing task performance refers to the ability to perform tasks that require the nursing process and provide effective patient care [ 13 , 14 , 15 ]. Nursing task performance has a significant impact on the quality of nursing and nursing competency [ 15 , 16 ]. Nursing competency—an integrated or effective performance required for nurses’ roles in the work setting [ 17 , 18 ]—is positively affected by ethical climate [ 8 ]. Numminen et al.’s study of 318 newly graduated nurses showed that the hospital ethical climate is positively correlated with nurse competency [ 8 ]. However, some studies have reported that nurses with less than one year of work experience are susceptible to hospital ethical climates [ 8 , 19 ]. Thus, this study included nursing task performance as a variable in our exploration of hospital ethical climate with nurses who had been working for more than one year to compare our work with previous findings.

Hospital ethical climate, nursing task performance, and critical thinking disposition

Critical thinking disposition can be defined as a person’s consistent internal motivation to solve problems and make decisions by thinking critically [ 20 ], and a measure to a tendency towards critical thinking [ 20 , 21 ]. Critical thinking disposition is an attitude to actively engage in critical thinking in situations that require critical thinking [ 21 ]. Nurses with a higher critical thinking disposition solve clinical problems to search for the cause and make decisions with careful consideration based on clinical evidence [ 22 ]. In particular, in rapidly changing clinical settings, nurses experience ethical dilemmas in the relationships with patients, colleagues, and organization, which are key elements of the hospital ethical climate, and this has been shown to be a factor that causes difficulties in nursing task performance and inhibiting nurse’s professional decision-making [ 1 ]. In addition, owing to the emergence of new infectious diseases such as COVID-19, nurses face more complex and high-level ethical challenges including fear of infection, disappointing results of treatment, and high mortality rate [ 23 ], and they are required to think critically to make decisions appropriate for these ethical situation. In Yuxiu Jia et al.’s qualitative study, nurses were reported to develop nursing strategies rooted in critical thinking to cope with ethical challenges [ 23 ], influenced by the hospital ethical climate [ 1 , 8 ]. Furthermore, some studies reported that critical thinking disposition is significantly associated with nursing task performance [ 24 , 25 ]. Choi & Cho's study targeting 419 nurses in a general hospital, critical thinking disposition and problem-solving processes were found to be factors that significantly affect nursing task performance [ 24 ]. In rapidly changing clinical setting, the study reported that critical thinking disposition of nurses is the one of the most important ability in resolving various ethical issues or dilemmas that arise during the process of nursing to patients from diverse cultural, social, and religious backgrounds [ 24 ]. Based on existing findings, we theorized that hospital ethical climate and nursing task performance may be related in a pathway through critical thinking disposition among nurses with one year of experience in a clinical setting.

Despite the need to consider hospital ethical climate, in a scoping review on ethical climate in the nursing environments, South Korea has the least amount of research compared to other countries [ 4 ], and interest in nurses’ perception of the ethical climate has not sufficiently spread in South Korea [ 6 ]. Moreover, the higher the critical thinking disposition, the higher the nurses’ decision making [ 22 ]. Therefore, to provide quality care to patients through accurate judgment in clinical settings, a disposition toward critical thinking and nursing task performance is important for nurses. However, to the best of our knowledge, no previous studies have examined the relationship between these three variables. Thus, we addressed this gap in the literature by investigating the relationship between hospital ethical climate, critical thinking disposition, and nursing task performance.

Research design

We conducted a cross-sectional survey. This study aimed to investigate the association between hospital ethical climate, critical thinking disposition, and nursing task performance, and confirm the mediating effect of critical thinking disposition on these relationships (Fig.  1 ).

Study model

Settings and participants

Nurses working in cities in Ulsan, South Korea were recruited using convenience sampling. Based on prior studies, nurses with less than one year of work experience were susceptible to the hospital ethical climate [ 8 , 19 ]; therefore, in this study, the inclusion criteria for the participants were nurses with more than one year of experience working in a general hospital. For regression analysis, the sample size was calculated using G*Power 3.1.9.7. The minimum number of participants needed for a statistical power of 0.95, a significance level of 0.05, and 12 predictors based on an effect size of 0.15, was 184. Considering an expected dropout rate of 20%, 220 printed questionnaires with consent forms were distributed and returned. A total of 200 valid questionnaires were used in the final analysis, after excluding 20 questionnaires with missing data.

Instruments

Hospital ethical climate.

Hospital ethical climate was measured using the Korean version of the Hospital Ethical Climate Survey (HECS) for Nurses developed by Olson [ 7 ]. Hwang and Park translated and validated the scale [ 26 ]; it comprises 26 items across five components: relationship with peers (four items), relationship with patients (four items), relationship with managers (six items), relationship with physicians (six items), and relationship with hospital/organization (six items). The responses are assessed on a 5-point Likert scale (ranging from 1 = “almost never true” to 5 = “almost always true”). A higher score indicated a more positive the perception of the hospital ethical climate. Cronbach’s alpha was 0.91 in Olson’s study, 0.95 in Hwang and Park’s study, and 0.92 in this study.

Critical thinking disposition

The Critical Thinking Disposition Scale, developed by Yoon [ 21 ] and validated by Shin, Park, and Kim [ 27 ] was used to measure critical thinking disposition. The scale comprises 27 items and seven categories: intellectual eagerness/curiosity (five items), prudence (four items), self-confidence (four items), systematicity (three items), intellectual fairness (four items), healthy skepticism (four items), and objectivity (three items). Each item is rated on a 5-point Likert scale (1 = do not agree at all, 5 = absolutely agree), and a higher score with a total score ranging from 27 to 135. Higher total or item scores indicate a higher critical thinking disposition. Two negatively worded items were reverse scored. Cronbach’s alpha was 0.84 in Yoon’s study and.90 in this study.

Nursing task performance

The Nursing Task Performance Scale developed by Paik, Han, and Lee was used to measure task performance among clinical nurses [ 28 ]. The scale comprises 35 items in four categories: knowledge-related nursing task performance (eight items), attitude evaluation regarding passion on nursing task performance (13 items), skills for nursing task performance (seven items), and evaluation of nursing ethics levels (seven items). Each item is rated on a 5-point Likert scale (1 = not at all, 5 = always). The higher total and item scores indicating higher nursing task performance. Cronbach’s alpha was 0.97 in Pack, Han and Lee’s study, and 0.96 in this study.

The measured covariates included age, sex, marital status, religion, education level, total duration of clinical experience, department, recognition of the Korean code of ethics for nurses, and education on nursing ethics.

Survey data collection and procedure

Survey data were collected between November and December 2021. First, permission for the study was obtained from the chief nursing department of each hospital. Thereafter, one of the researchers contacted the nurses directly at each hospital and explained the study’s purpose, procedure, and questionnaire content. Moreover, nurses were informed that participation was voluntary and that they could withdraw at any time during the study without any negative consequences. The questionnaires were then distributed along with a consent form, and those who did not understand the items in the questionnaires could ask the researcher for help to fill them out. One of the researchers collected the completed questionnaires. In the case of another hospital, we provided the URL for the survey using Google Surveys owing to the risk of COVID-19. We uploaded the same questionnaires to a Google survey, and the first page of the survey contained the purpose, procedure, voluntary nature, and withdrawal from the study. In addition, at the bottom of the first page, a button (“I agree”) was created, and clicking it would denote that the participants has agreed to participate in the study. For those who did not understand the items in the questionnaire, the contact number and email were provided on the first page, and the researchers responded and explained the study whenever the participants requested.

Data analysis

Data were analyzed using SPSS (version 25.0; IBM Corp., Armonk, NY, USA) and the SPSS PROCESS macro v3.4. Skewness and kurtosis for each main variable (critical thinking disposition, hospital ethical climate, and nursing task performance) were checked to determine whether the data were normally distributed (skewness range of all main variables -0.121 to 0.347, kurtosis range of all main variables -0.357 to 0.330). The main variables and covariates were analyzed using descriptive statistics. Correlations between the study variables were analyzed using Pearson’s correlation coefficients. PROCESS macro for SPSS (Model 4) was used to evaluate the mediating effect of hospital ethical climate on the relationship between critical thinking disposition and nursing task performance [ 29 , 30 ]. A 95% bias-corrected confidence interval from 5,000 resamples was generated using the bias-corrected bootstrapping method. The bootstrapping size was 5,000. Significant indirect effects were identified as p < 0.05 when the confidence interval (CI) did not include zero [ 29 , 30 ]. For analysis of correlations and mediating effect, main study variables was used the item scores.

Ethical consideration

This study was approved by the Institutional Review Board of Dongguk University, to which the authors belong (DGU IRB 20210040). This study was conducted on human participants in accordance with the Declaration of Helsinki and its subsequent amendments. The purpose, procedures, and rules of the study were explained to all the participants. In addition, the voluntary nature and confidentiality of the study were highlighted, and participants’ personal information was not revealed. Informed consent was obtained from all the subjects.

General characteristics

Of the 200 participants, 92.0% (184) were female, and the mean age was 30.50 years (range 23–64). A total of 142 (71.0%) participants were unmarried and 27.5% (55) were religious. A total of 141 (70.5%) participants held a bachelor’s degree or higher. The mean total period of clinical experience was 7.38 years (range 1–32), about half of the participants had worked in a general ward (56%), and 163 participants responded that their positions were staff nurses. A total of 117 participants were aware of the Korean code of ethics for nurses (58.5%), and 69.5% of the participants responded that they had experience receiving nursing ethics education (Table  1 ).

Descriptive statistics and correlations between hospital ethical climate, critical thinking disposition, and nursing task performance

The total score of hospital ethical climate was 91.86 ± 11.29. The mean scores of hospital ethical climate and critical thinking disposition were 3.53 ± 0.43 and 3.62 ± 0.40, respectively (Table  2 ). The mean nursing task performance score was 3.96 ± 0.43. The higher mean score for hospital ethical climate was peer and manager, 3.92 ± 0.47 and 3.80 ± 0.53, respectively. Hospital ethical climate was positively correlated with critical thinking disposition (r = 0.37, p  < 0.001) and nursing task performance (r = 0.57, p  < 0.001). In addition, nursing task performance was positively correlated with critical thinking disposition (r = 0.64, p  < 0.001).

Mediating effect of critical thinking disposition on the relationship between hospital ethical climate and nursing task performance

As shown in Table  3 , the direct association between hospital ethical climate and nursing task performance was significant (ß = 0.34, p  < 0.001). In the mediation analysis, hospital ethical climate was positively associated with critical thinking disposition (ß = 0.30, p  < 0.001), and critical thinking disposition was positively associated with nursing task performance (ß = 0.54, p  < 0.001). The indirect pathway of hospital ethical climate on nursing task performance through critical thinking disposition was significant (index = 0.16; Boot SE = 0.04; Boot CI:0.09, 0.25). Figure  2 shows the indirect pathway for critical thinking disposition on the relationship between hospital ethical climate and nursing task performance.

The indirect pathway of hospital ethical climate on the relationship between critical thinking disposition and nursing task performance (*** p  < .001)

The findings of this study show that hospital ethical climate has a positive association with nursing task performance and that critical thinking disposition has a mediating effect on this relationship. This study makes an important contribution to the literature, given that it is the first to evaluate the association between hospital ethical climate, critical thinking disposition, and nursing task performance, and the mediating effect of critical thinking disposition on the relationship between hospital ethical climate and nursing task performance in nurses in Korea.

Participants in this study evaluated the hospital ethical climate positively and higher than neutral with a total sum of 91.86, which is in accordance with previous studies [ 8 , 19 , 31 ]. In addition, domains that were positively perceived in the hospital ethical climate were particularly related to peers and managers rather than patient, hospital/organization and physicians. The results of this study are consistent with earlier studies [ 24 ]. Nurse managers are commonly appointed from among the nursing staff in the hospital, and most have a long-term clinical background and a good understanding of the hospital’s ethical climate in the field [ 11 ]. This makes managers willing to listen and support staff nurses in decision-making when they face ethical dilemmas regarding a nursing situation. Through this process, staff nurses come to trust and respect their managers, which has a crucial impact on creating and maintaining positive perceptions of the hospital’s ethical climate. Moreover, their leadership and support to staff nurses are related to the hospital’s ethical climate and, consequently, how ethical issues are dealt with for the benefit of patients [ 8 ]. The previous study reported that access to knowledgeable peers for decision support on ethical issues is important resources for preventing and handling ethical conflicts [ 32 ]. In addition, it is reported that after particularly difficult events, when reflecting whit colleagues, action, feeling, and new perspectives on ethical conflicts are made visible, processed, and normalized [ 32 ]. Thus, the exchange of experience and judgements between peers contribute to self-confidence and the ability to act in ethical conflicts.

The relationship among hospital ethical climate, critical thinking disposition, and nursing task performance

In this study, nurses with a positive perception of their hospital’s ethical climate showed increased nursing task performance. Although it is difficult to compare our results with those in currently published literature, few studies have examined the relationship between hospital ethical climate and nursing task performance. Numminen et al. showed that newly graduate nurses who had a positive perception of hospital ethical climate had significantly higher nursing competency [ 8 ]. In addition, nurses with a more positive perception of the “patient” dimension of hospital ethical climate were less likely to have made medical errors [ 26 ]. Considering job satisfaction and turnover as factors affecting nursing competency, including nursing task performance, a previous study reported that hospital ethical climate was positively correlated with job satisfaction [ 10 ]. Other studies found that nurses with more negative perceptions of hospital ethical climate were highly inclined to leave the hospital or their previous position [ 19 , 33 ]. Moreover, a negative or poor ethical climate can contribute to burnout [ 12 ]. Job satisfaction, intent to leave, and burnout are associated with lower nursing task performance or nursing competency correlated with hospital ethical climate [ 8 , 34 , 35 ] and result in poor patient safety and quality of care [ 34 ]. Therefore, hospital administrators should pay attention to a more positive institutional ethical climate.

Critical thinking disposition was significantly positively associated with nursing task performance, and this is consistent with previous findings [ 24 , 25 ]. In a study by Mohamed et al., critical thinking disposition was significantly correlated with nursing performance in patients undergoing hemodialysis [ 25 ]. Moreover, in Park et al.’s study of 188 nurses with more than 13 months of clinical experience, critical thinking disposition was a major factor influencing nurses’ competency as measured using the nursing performance appraisal tool [ 36 ]. Dispositions are the tendency to do something, and critical thinking disposition is included in the concept of critical thinking [ 37 ]. In addition, critical thinking does not occur or may be substandard without critical thinking disposition [ 38 ]. In nursing, nurses with higher critical thinking or critical thinking dispositions, are able to perform their professional work efficiently and provide effective nursing care [ 24 , 36 , 39 ]. Thus, helping nurses increase their critical thinking disposition enables them to engage proactively in job performance.

Critical thinking disposition mediated the relationship between hospital ethical climate and nursing task performance. It was confirmed that a more positive perception of the hospital’s ethical climate was associated with increased critical thinking disposition, which subsequently increased nursing task performance. In addition, nurses who perceived the hospital’s ethical climate as more negative decreased nursing task performance with decreasing critical thinking disposition. This suggests that critical thinking disposition is an important factor in the hospital ethical climate and nursing task performance, which can be explained by several factors. Given that the hospital ethical climate sets standards for how problems should be addressed, focusing on interactions with colleagues and patients [ 7 ], when nurses perceive a more positive hospital ethical climate, their communication self-efficacy increases [ 40 ], and when communication competency increases, critical thinking disposition increases [ 41 ]. In other words, nurses perceived the hospital’s ethical climate positively and actively communicated with other professionals, including managers or physicians, about patient care, treatment, or further treatment plans, and an increase in critical thinking disposition in the process of exchanging opinions. The higher the critical thinking disposition, the higher the nurse’s critical decision-making [ 22 ] and the nursing task performance [ 36 , 39 ]. Therefore, in order to improve nurses’ task performance, the first step would be to improve the hospital ethical climate more positively. Organizations can improve their hospital ethical climate through ethics training, support, and information exchange within the nursing team [ 4 ]. Moreover, the code of ethics for nurses is to be built upon in combination with the laws, regulation and professional standards [ 42 ], and culture plays an important role in giving shape to nursing professional ethical values [ 43 ]. Therefore, to develop ethical training/education for nurses, there should be mandated and customized by the local law and culture. Together with this, critical thinking disposition is also an important factor to consider improving nurses’ task performance, and it is important to provide various training or education programs to improve critical thinking disposition. Hospital policymakers or administrators should identify the characteristics of the hospital ethical climate and create a positive hospital ethical climate, as well as increase nurses’ critical thinking disposition and improve task performance. It also enhances quality of care and patient safety.

Limitations

This study had some limitations. First, the findings have limited generalizability because the nurses were conveniently sampled. Second, this was a cross-sectional study, which limits the interpretation of causality. Hence, future research can be improved through longitudinal studies. Third, as some responses were made through an online self-report questionnaire, participants may have exaggerated or reduced their performance and perceptions according to their understanding. Additionally, we did not consider the number of hospitals or universities involved in nursing ethics education. This may have affected nurses’ perceptions of the hospital’s ethical climate. Future research should test this hypothesis, including the number and places of nursing ethics education. Finally, numerous factors influenced nursing task performance, and only hospital ethical climate and critical thinking dispositions were included in this study. Hospital ethical climate and critical thinking disposition could explain a limited portion of nursing task performance. Hence, further research is recommended to explore various factors affecting nursing task performance.

The results of this study indicated that nursing task performance was significantly influenced by hospital ethical climate, and the “hospital/organization and physicians” domain was lower than other domains in the hospital ethical climate. To improve a hospital’s ethical climate, small meetings or conferences should be held periodically to exchange opinions and experiences with physicians and nurses regarding patient care and ethical issues. Increasing the number of nursing staff may also be considered to address patients’ needs and health expectations. In addition, there are different action proposed related on other domains, e.g., the workshop, seminars, or periodic counseling to develop leadership competencies among nurse [ 44 ], in-service training which adjusted for the hospital/organization to enhance nurses’ perception of the ethical climate [ 31 ]. Moreover, sufficient publicity and related education should be provided so that nurses can be aware of the ethical ideology pursued by the organization and achieve ideological alignment [ 32 ]. Critical thinking disposition mediates the relationship between hospital ethical climate and nursing task performance. Therefore, to enhance nursing task performance, hospital administrators should provide training programs or education related to critical thinking while making efforts to create a positive ethical hospital climate.

Availability of data and materials

The datasets analyzed during the current study are not publicly available because of privacy or ethical restrictions but are available from the corresponding author upon reasonable request.

The authors declare no competing interests.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Hypothesis testing

• PMID: 8900794
• DOI: 10.1097/00002800-199607000-00009

Hypothesis testing is the process of making a choice between two conflicting hypotheses. The null hypothesis, H0, is a statistical proposition stating that there is no significant difference between a hypothesized value of a population parameter and its value estimated from a sample drawn from that population. The alternative hypothesis, H1 or Ha, is a statistical proposition stating that there is a significant difference between a hypothesized value of a population parameter and its estimated value. When the null hypothesis is tested, a decision is either correct or incorrect. An incorrect decision can be made in two ways: We can reject the null hypothesis when it is true (Type I error) or we can fail to reject the null hypothesis when it is false (Type II error). The probability of making Type I and Type II errors is designated by alpha and beta, respectively. The smallest observed significance level for which the null hypothesis would be rejected is referred to as the p-value. The p-value only has meaning as a measure of confidence when the decision is to reject the null hypothesis. It has no meaning when the decision is that the null hypothesis is true.

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