P-values in nursing research matter because they appear in SPSS output, journal articles, dissertations, theses, evidence-based practice projects, quality improvement reports, and nursing research results chapters. Yet many students are unsure what a p-value means, how to interpret it, and how to report it correctly.
A p-value helps students judge whether a finding is statistically significant. However, it does not prove that an intervention worked, does not show clinical importance by itself, does not prove causation, and does not tell the full story of a study. A p-value should be interpreted with the research question, study design, sample size, effect size, confidence interval, assumptions, clinical relevance, and limitations.
P-values belong mainly to inferential statistics. They are different from descriptive statistics, which summarize data using frequencies, percentages, means, medians, and standard deviations. Students who need a broader overview can read Types of Data Analysis in Research, Types of Data Analysis in Quantitative Research, and Inferential Data Analysis in Nursing Research.
This guide explains p-values clearly and practically for nursing students writing research proposals, dissertations, theses, capstones, EBP projects, QI evaluations, and research papers.
What Is a P-Value in Nursing Research?
A p-value is the probability of observing data as extreme as, or more extreme than, the study results if the null hypothesis were true, assuming the statistical model and assumptions are appropriate. This definition is consistent with the American Statistical Association’s explanation of p-values and statistical significance (Wasserstein & Lazar, 2016).
In simpler student language, a p-value helps answer this question:
If there were truly no difference, no relationship, or no effect in the population, how unusual would these study results be?
A small p-value suggests that the observed result would be unlikely if the null hypothesis were true. A larger p-value suggests that the observed result is more compatible with the null hypothesis. However, a p-value is not the probability that the null hypothesis is true.
In nursing research, p-values may be used when comparing pain scores before and after an intervention, testing whether medication adherence improved after patient education, examining whether burnout scores differ across nursing units, testing whether health literacy is related to adherence, or examining whether readmission rates differ between two groups.
A p-value is part of inferential statistics, not descriptive statistics. Descriptive analysis can show that one group had a higher mean pain score than another group. Inferential analysis, using a p-value, helps assess whether that observed difference is statistically significant.
Why P-Values Matter in Nursing Research
P-values matter because nursing students often need to decide whether observed results provide statistical evidence against the null hypothesis. This is common in hypothesis testing, intervention studies, survey research, EBP projects, QI evaluations, clinical education research, healthcare outcome studies, and dissertation results chapters.
For example, a student may evaluate whether a pressure injury prevention education program improved nurses’ knowledge scores. A p-value can help determine whether the observed pre-test and post-test difference is statistically significant.
Another student may examine whether patient satisfaction scores differ between two hospital units. The p-value can help determine whether the difference is statistically significant, but the student still needs to consider the size of the difference, sample size, response rate, and clinical meaning.
P-values are useful, but they are not enough. The ASA cautions that scientific conclusions should not be based only on whether a p-value crosses a threshold such as .05 (Wasserstein et al., 2019). Nursing students should interpret p-values alongside effect sizes, confidence intervals, design quality, practical relevance, and limitations.
P-Values and Hypothesis Testing
Hypothesis testing is one of the main places where p-values appear in nursing research. It helps students decide whether the data provide enough statistical evidence to reject the null hypothesis.
Null and Alternative Hypotheses
The null hypothesis usually states that there is no difference, no relationship, or no effect. The alternative hypothesis states that there is a difference, relationship, or effect.
Example:
Null hypothesis: There is no difference in medication adherence scores before and after patient education.
Alternative hypothesis: Medication adherence scores differ before and after patient education.
Significance Level and Alpha
The significance level, often called alpha, is the threshold the researcher chooses before analysis. The most common alpha level is .05.
If p < .05, the result is often described as statistically significant. If p > .05, the result is usually described as not statistically significant. However, .05 is a convention, not a magic line that separates truth from falsehood.
Rejecting or Failing to Reject the Null Hypothesis
If the p-value is smaller than the alpha level, the student may reject the null hypothesis. If the p-value is larger than the alpha level, the student fails to reject the null hypothesis.
Students should avoid saying they “accept” the null hypothesis. A non-significant result means the study did not find statistically significant evidence against the null hypothesis. It does not prove that no effect or relationship exists.
Type I and Type II Errors
A Type I error occurs when the researcher rejects the null hypothesis when it is actually true. In simple terms, this means finding a statistically significant result when there is no true effect.
A Type II error occurs when the researcher fails to reject the null hypothesis when a true effect exists. This can happen when the sample size is too small, the measurement is weak, or the study has low statistical power.
How P-Values Fit Into Hypothesis Testing
| Step | What the student does | Nursing research example | Common mistake to avoid |
|---|---|---|---|
| 1. State the question | Identify what is being tested | Does patient education improve adherence? | Starting with SPSS instead of the research question |
| 2. Define hypotheses | State null and alternative hypotheses | Null: no adherence difference before and after education | Writing vague or untestable hypotheses |
| 3. Choose alpha | Set the significance level | α = .05 | Changing alpha after seeing results |
| 4. Select the test | Choose a test that fits the design and data | Paired-samples t-test for pre/post scores | Using the wrong test for the variable type |
| 5. Run analysis | Obtain the p-value | SPSS reports p = .032 | Copying raw output without interpretation |
| 6. Interpret result | Compare p-value to alpha | p = .032 is below .05 | Saying the p-value proves the intervention worked |
| 7. Report carefully | Connect result to the research question | Adherence scores increased significantly | Ignoring effect size or clinical meaning |
How to Interpret P-Values in Nursing Research
Interpreting p-values requires more than checking whether the value is below .05. Students should explain what the result means in relation to the research question and what it does not prove.
p < .05
A p-value below .05 is usually considered statistically significant. This means the observed result is unlikely under the null hypothesis, assuming the model and assumptions are appropriate.
Example: In a pre-test/post-test medication adherence study, p = .032 suggests that the change in adherence scores was statistically significant at the .05 level.
The student may write that the results showed a statistically significant change in adherence scores. The student should not write that the intervention definitely caused the change unless the study design supports a causal claim.
p = .05
A p-value exactly at .05 should be interpreted cautiously. Students should follow their planned alpha level and supervisor guidance, but they should avoid treating p = .049 and p = .051 as completely different findings.
A p-value close to .05 should be discussed with attention to effect size, confidence interval, sample size, measurement quality, and clinical meaning.
p > .05
A p-value above .05 is usually described as not statistically significant. This does not mean the result is useless. It means the study did not find statistically significant evidence against the null hypothesis at the selected alpha level.
Example: If p = .614 in a patient satisfaction comparison, the student may state that the difference in satisfaction scores between groups was not statistically significant.
p < .001
A p-value below .001 means the result is very unlikely under the null hypothesis. APA Style recommends reporting p < .001 rather than p = .000 (American Psychological Association, 2024).
Example: If burnout scores differ across nursing units with p < .001, the student may state that there was a statistically significant difference in burnout scores across units.
Examples of P-Value Interpretation in Nursing Research
| P-value | Basic interpretation | What the student may say | What the student should avoid saying |
|---|---|---|---|
| p = .032 | Statistically significant at .05 | Medication adherence scores increased significantly after education | The program definitely caused improvement |
| p = .614 | Not statistically significant | Satisfaction scores did not differ significantly between groups | There was no difference at all |
| p < .001 | Strong statistical evidence against the null | Burnout scores differed significantly across units | The finding is automatically clinically important |
| p = .048 | Statistically significant but close to .05 | The result was statistically significant, but interpretation should consider sample size and effect size | This proves the hypothesis is true |
| p = .071 | Not statistically significant at .05 | The study did not find statistically significant evidence of a difference | The intervention failed completely |
| p = .200 | Not statistically significant | The observed difference was not statistically significant | The null hypothesis is true |
What P-Values Do Not Mean
P-values are widely misunderstood. Nursing students should know what a p-value does not mean.
A P-Value Is Not the Probability That the Null Hypothesis Is True
A p-value does not tell the probability that the null hypothesis is true. It tells how compatible the observed data are with the null hypothesis, assuming the statistical model and assumptions are correct.
A P-Value Is Not the Probability That the Alternative Hypothesis Is True
A p-value also does not tell the probability that the alternative hypothesis is true. It is not a direct probability of either hypothesis.
A P-Value Is Not the Size of the Effect
A smaller p-value does not automatically mean a bigger effect. A tiny difference can become statistically significant in a large sample. A larger practical difference may fail to reach significance in a small sample.
A P-Value Does Not Show Clinical Importance
A result can be statistically significant but clinically weak. For example, a pain score reduction of 0.2 points may be statistically significant in a very large sample but may not matter to patients.
A P-Value Does Not Prove an Intervention Works
If medication adherence improved with p = .032, the student may report statistical significance. However, whether the intervention “worked” depends on design quality, comparison group, effect size, clinical importance, implementation, and limitations.
A P-Value Does Not Prove Causation
A significant association between nurse burnout and turnover intention does not prove that burnout caused turnover intention. Causation depends on study design and evidence.
A P-Value Does Not Prove Generalizability
A significant result from one small hospital, university, ward, or clinic does not automatically apply to all patients, nurses, or healthcare settings.
A P-Value Does Not Prove Study Quality
A poorly designed study can produce a significant p-value. Research quality also depends on sampling, measurement, bias, assumptions, ethics, reporting, and interpretation.
A Non-Significant P-Value Does Not Mean the Result Is Useless
Non-significant findings can still be valuable. They may show uncertainty, limited power, measurement issues, or areas needing further research.
Statistical Significance vs Clinical Significance
Statistical significance and clinical significance are not the same. Clinical significance concerns whether the finding matters in practice, patient care, education, policy, or quality improvement. Statistical significance concerns the strength of evidence against the null hypothesis.
A statistically significant result may not always be clinically meaningful. A clinically meaningful result may not always reach statistical significance, especially in a small sample.
The Cochrane Handbook notes that a small p-value in a large study can reflect a trivial effect that may not produce meaningful benefit for patients (Cochrane, 2024).
| Feature | Statistical significance | Clinical significance | Nursing research example |
|---|---|---|---|
| Main concern | Whether the result is unlikely under the null hypothesis | Whether the result matters in practice | Pain score difference is statistically significant but very small |
| Main indicator | P-value | Effect size, clinical judgment, patient impact, MCID where available | Medication adherence improves enough to affect patient self-management |
| Depends on | Sample size, variability, test assumptions, effect | Patient outcomes, safety, feasibility, cost, quality | A fall reduction may matter even if p > .05 in a small project |
| Common mistake | Treating p < .05 as automatically important | Ignoring statistical uncertainty | Calling a tiny significant change “clinically meaningful” |
| Better practice | Report p-value with effect size and confidence interval | Discuss patient, practice, or service meaning | Interpret burnout-score differences beyond the p-value |
P-Values, Confidence Intervals, and Effect Sizes
P-values should not be interpreted alone. Students should also consider confidence intervals and effect sizes.
Confidence Intervals
A confidence interval shows a range of plausible values for the estimate, based on the data and model. It helps students understand uncertainty and precision.
For example, if a study estimates the mean difference in pain scores after an intervention, the confidence interval shows the range of plausible mean differences. A narrow interval suggests more precision. A wide interval suggests more uncertainty.
Effect Sizes
An effect size describes the magnitude of a difference, relationship, or effect. It helps students answer the question: How large is the finding?
Sullivan and Feinn (2012) explain that p-values do not show the size of an effect, so effect sizes are needed to understand practical meaning (Sullivan & Feinn, 2012).
Precision, Magnitude, and Practical Meaning
A p-value tells students about statistical evidence against the null hypothesis. An effect size helps show magnitude. A confidence interval shows uncertainty around the estimate.
For example:
A medication adherence intervention may have p = .018, but the effect size tells whether the improvement was small, moderate, or large.
A readmission odds ratio may be statistically significant, but the confidence interval shows how precise the estimate is.
A correlation between burnout and job satisfaction may have p < .001, but the correlation coefficient shows the strength and direction of the relationship.
A pain-score difference may be statistically significant, but the mean difference and confidence interval help students judge whether the change matters to patients.
P-Values and Sample Size
Sample size affects p-values. Large samples can make very small effects statistically significant. Small samples may fail to detect meaningful effects.
This matters in nursing dissertations because student projects often have small samples. A small capstone or DNP project may show improvement in patient education scores, but the p-value may be above .05 because the sample is too small or the study has low statistical power.
Low statistical power increases the risk of Type II error. This means the study may fail to find statistical significance even when a meaningful effect exists.
Students should not interpret p-values without considering sample size, design, measurement quality, variability, and practical meaning.
Common Statistical Tests That Produce P-Values
Many inferential tests generate p-values. Students may see p-values in output from:
- independent-samples t-tests
- paired-samples t-tests
- chi-square tests
- ANOVA
- Pearson correlation
- Spearman correlation
- Mann-Whitney U tests
- Wilcoxon signed-rank tests
- regression
- logistic regression
The correct test depends on the research question, variable type, study design, sample size, and assumptions. Students who need a broader guide to statistical testing can read Inferential Data Analysis in Nursing Research.
P-Values in SPSS Output
SPSS may label p-values in different ways. Students may see:
- Sig.
- Sig. (2-tailed)
- Asymp. Sig.
- Exact Sig.
- p-value in model output
The correct p-value depends on the test and table. Students should not report every “Sig.” value they see. They should identify the value that answers the research question.
Students should also avoid copying raw SPSS output directly into a dissertation. SPSS output should be translated into clean tables and plain-language interpretation.
Students who need help finding the correct p-value, cleaning SPSS tables, or interpreting output can visit SPSS Data Analysis Help.
How to Report P-Values in a Nursing Dissertation
P-values should be reported clearly in the results chapter. A strong report names the test, gives descriptive statistics first, reports the test statistic, includes degrees of freedom where relevant, reports the p-value, includes an effect size where appropriate, and interprets the finding in relation to the research question.
Students should avoid reporting a p-value alone. A sentence such as “p = .032” does not explain what was tested or what the result means.
What to Include
When reporting p-values, include:
- the test used
- group means, standard deviations, frequencies, or percentages where relevant
- test statistic
- degrees of freedom where relevant
- exact p-values when possible
- p < .001 when very small
- effect size where relevant
- plain-language interpretation
- link back to the research question or hypothesis
Correct Reporting Examples
A paired-samples t-test showed a statistically significant increase in medication adherence scores after the education intervention, t(df) = X.XX, p = .032.
The association between health literacy and medication adherence was statistically significant, χ²(df, N = X) = X.XX, p = .018.
The difference in patient satisfaction scores between the two groups was not statistically significant, p = .614.
A one-way ANOVA showed a statistically significant difference in burnout scores across nursing units, F(df, df) = X.XX, p < .001.
Students should replace X values with the actual study output.
APA Style Tips for Reporting P-Values
APA-style reporting helps students present p-values consistently and professionally.
APA Style recommends using lowercase italic p, reporting exact p-values when possible, using p < .001 for very small values, and avoiding p = .000 (American Psychological Association, 2024).
Students should maintain consistent decimal places, report p-values in text or tables clearly, and avoid overstatement. P-values should not be used as the only evidence supporting a conclusion.
For example:
Correct: p = .032
Correct: p < .001
Incorrect: p = 0.000
Incorrect: p value proves the intervention worked
Students can also review Best Practices for Data Analysis for broader guidance on reporting and interpretation.
Common Mistakes Students Make With P-Values
One common mistake is saying the p-value proves the hypothesis is true. It does not.
Another mistake is saying p = .04 means there is a 4% chance the result is due to chance. That is not correct.
Students may treat p < .05 as automatically important. Statistical significance does not guarantee clinical significance.
Many students ignore effect size. A p-value may show statistical evidence, but it does not show magnitude.
Students also ignore confidence intervals. Without a confidence interval, it is harder to understand precision and uncertainty.
Ignoring sample size is another problem. Large samples can make small differences significant, while small samples may miss meaningful effects.
Some students ignore test assumptions. If assumptions are not met, the p-value may be misleading.
Reporting p = .000 from SPSS is also incorrect. APA-style reporting uses p < .001.
Students may claim causation from a significant p-value. A significant association does not prove cause-and-effect.
Another mistake is dismissing non-significant findings completely. A non-significant result may still be informative.
Students also report p-values without explaining what they mean. Every p-value should be connected to the research question.
P-values should not be used in qualitative research unless the study includes a quantitative component. Qualitative themes are interpreted through qualitative rigor, not p-values.
Finally, selecting only significant results to report is a transparency problem. Planned analyses should be reported honestly, whether significant or not.
How to Write About Non-Significant P-Values
Non-significant findings are common in nursing research. Students should report them honestly and interpret them carefully.
A non-significant p-value does not automatically mean there was no effect, no relationship, or no difference. It means the study did not find statistically significant evidence at the selected alpha level.
For example, a patient education intervention may improve medication adherence scores, but p = .071. The student should not write, “The intervention had no effect.” A better statement is:
Medication adherence scores improved after the intervention, but the change was not statistically significant, p = .071. This finding should be interpreted cautiously because the sample size was small, and the study may have had limited statistical power.
Students should consider sample size, power, direction of effect, confidence intervals, measurement quality, missing data, and clinical relevance. Non-significant results should not be hidden.
Ethical and Transparent Use of P-Values
P-values should be used responsibly. Ethical statistical reporting supports research integrity and dissertation credibility.
Students should avoid p-hacking, which means running many analyses or changing decisions until a significant result appears. They should also avoid selective reporting, changing hypotheses after seeing results, hiding non-significant findings, or presenting exploratory analyses as if they were planned.
The ASA warns against using p-values as a mechanical decision rule for scientific conclusions (Wasserstein & Lazar, 2016). Transparent reporting requires planned analyses, honest limitations, and careful explanation of uncertainty.
Students should report findings even when results are not significant. They should also explain limitations such as small sample size, missing data, measurement limitations, assumption issues, or weak design.
When to Get Help Interpreting P-Values
Students may need help interpreting p-values when SPSS output is confusing, they are not sure which p-value to report, supervisors request corrections, or statistical significance is unclear.
Support may also be useful when students struggle to interpret non-significant findings, connect p-values to research questions, report APA-style results, explain confidence intervals or effect sizes, or write the nursing research results chapter.
Students who need support can request Dissertation Data Analysis Help. Those who need help interpreting SPSS output can visit SPSS Data Analysis Help. For broader proposal, methodology, results, or discussion support, visit Nursing Dissertation Help.
Conclusion
Understanding p-values in nursing research helps students interpret inferential statistics, report findings clearly, and avoid common statistical mistakes. A p-value can show whether study results are statistically significant, but it cannot prove importance, causation, clinical value, or truth by itself.
Strong p-value interpretation requires the research question, effect size, confidence interval, sample size, assumptions, study design, clinical significance, and limitations. Students should avoid treating p < .05 as a magic rule and should report non-significant findings honestly.
If you are unsure how to interpret or report p-values in a dissertation, thesis, capstone, EBP project, QI evaluation, or nursing research paper, expert support can help you produce clearer and more defensible results.
FAQs
1. What is a p-value in nursing research?
A p-value is the probability of observing data as extreme as, or more extreme than, the study results if the null hypothesis were true, assuming the statistical model and assumptions are appropriate.
2. What does p < .05 mean?
p < .05 means the result is statistically significant at the .05 level. It suggests the observed result would be unlikely under the null hypothesis, assuming the test and assumptions are appropriate.
3. Does a p-value prove that an intervention worked?
No. A p-value does not prove that an intervention worked. Students must also consider study design, effect size, confidence interval, clinical relevance, and limitations.
4. What is the difference between statistical significance and clinical significance?
Statistical significance concerns evidence against the null hypothesis. Clinical significance concerns whether the finding matters in practice, patient care, safety, education, or service improvement.
5. Why should p-values be reported with effect sizes?
Effect sizes show the magnitude of a difference or relationship. A p-value may show statistical significance, but it does not show how large or meaningful the effect is.
6. What does a non-significant p-value mean?
A non-significant p-value means the study did not find statistically significant evidence against the null hypothesis at the selected alpha level. It does not prove that no effect exists.
7. How do I report p-values in APA style?
Use lowercase italic p, report exact p-values when possible, use p < .001 for very small values, and do not report p = .000.
8. Why should I not report p = .000?
SPSS may show .000, but the p-value is not exactly zero. APA-style reporting uses p < .001.
9. Can qualitative research use p-values?
Pure qualitative research does not usually use p-values. P-values belong to quantitative inferential analysis. Mixed methods studies may include p-values in the quantitative strand.
10. When should I get help interpreting p-values?
You should get help when SPSS output is confusing, you are unsure which p-value to report, your supervisor requests corrections, or you struggle to explain statistical significance in your results chapter.
References
American Psychological Association. (2024). Number and statistics guide: APA Style 7th edition.
Cochrane. (2024). Chapter 15: Interpreting results and drawing conclusions. Cochrane Handbook for Systematic Reviews of Interventions.
EQUATOR Network. (n.d.). Search for reporting guidelines.
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). Routledge.
Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer.
Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the p value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/JGME-D-12-00156.1
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108
Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “p < 0.05”. The American Statistician, 73(sup1), 1–19. https://doi.org/10.1080/00031305.2019.1583913
