Your output shows Sig. = .032. What does that number actually say? Most students learn a rule: below .05 means “significant”, above means “not significant”. The rule is easy to apply and easy to misread. This guide gives the p value explained in plain terms: what it measures, what it does not, and how to report and discuss it so that an examiner has nothing to correct.
This article is part of our guide How to Write a Master’s Thesis by the Mastermind PhD team and relates to our Statistical Analysis service. Sources last checked: November 2026.
What is a p value? A p value is the probability of obtaining a result at least as extreme as the one observed, assuming that the null hypothesis (for example, no difference between groups) and all the other assumptions of the statistical model are true. A small p value indicates that the data are unusual under that model; it does not give the probability that the null hypothesis is true.
How does a significance test produce a p value?
Every test follows the same logic:
- State a null hypothesis (H0): for example, the mean burnout score is the same for men and women.
- Choose a significance level (alpha) before seeing the data, conventionally .05.
- Compute a test statistic (t, F, chi-square) from your data.
- Find the p value: how often a statistic this large or larger would occur if H0 were true.
- If p ≤ alpha, reject H0 and call the result statistically significant; otherwise, do not reject it.
SPSS labels the p value as Sig. or, in recent versions, as One-Sided p and Two-Sided p. Use the two-sided value unless a one-sided test was justified in your proposal.
What a p value does not tell you
The American Statistical Association published a statement on p-values with six principles (Wasserstein & Lazar, 2016). In short:
| ASA principle | What it means for your thesis |
|---|---|
| 1. A p value shows how incompatible the data are with a specified model | It is about the data under H0, not about your theory directly |
| 2. It is not the probability that the hypothesis is true or that the data arose by chance alone | Do not write “there is a 3% chance the result is due to chance” |
| 3. Conclusions should not rest only on crossing a threshold | p = .049 and p = .051 are practically the same evidence |
| 4. Proper inference needs full reporting and transparency | Report all tests you ran, not only the significant ones |
| 5. A p value does not measure the size or importance of an effect | Always add an effect size |
| 6. By itself, a p value is not a good measure of evidence | Combine it with confidence intervals, design and prior findings |
Greenland et al. (2016) list many more misinterpretations; the most common in theses is reading a non-significant p value as proof that there is no effect.
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Why is .05 the threshold, and is it fixed?
The .05 convention comes from early twentieth-century practice, not from a law of nature. Some researchers have proposed a stricter .005 for new discoveries (Benjamin et al., 2018), and others argue against dichotomous significance altogether (Amrhein, Greenland, & McShane, 2019). For a thesis, use the alpha your faculty or field expects, state it in the methodology chapter, and apply it consistently. If you run many tests on the same hypothesis, explain whether you corrected for multiple comparisons, for example with a Bonferroni adjustment.
How to report a p value in APA style
- Report exact values to two or three decimals: p = .032, p = .270.
- For values below .001 write p < .001. Never write p = .000; a probability is never exactly zero.
- No leading zero, because a p value cannot exceed 1.
- Italicize p.
- Place it after the test statistic and before the effect size: t(198) = 2.16, p = .032, d = 0.31.
An illustrative example: “Teachers in private schools reported higher job satisfaction than teachers in public schools, t(198) = 2.16, p = .032, d = 0.31, a small effect.”
How to interpret a non-significant p value
A p value above .05 means the data are not unusual enough under H0 to reject it. It does not show that the groups are equal. Write “the difference was not statistically significant” rather than “there was no difference”, give the effect size and its confidence interval, and consider whether the sample had enough power to detect a meaningful effect. Equivalence testing is the right tool if you really need to show that an effect is negligible (Lakens, 2017).
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Frequently asked questions
Is a smaller p value a stronger effect?
No. A smaller p value can come from a larger sample with the same effect. The effect size, not the p value, describes strength.
What does p = .06 mean, “marginally significant”?
Many examiners dislike “marginally significant” or “a trend toward significance”. Report the exact p value, the effect size and the interval, and interpret them without changing the threshold after seeing the data.
Can I use a one-tailed test to get a smaller p value?
Only if the directional hypothesis and the one-tailed test were stated before data collection. Switching after the result is seen inflates the error rate and examiners treat it as a serious flaw.
Why does SPSS show .000?
SPSS rounds to three decimals. A value displayed as .000 is smaller than .0005 and must be reported as *p* < .001.
Should I report p values for descriptive statistics?
No. A p value belongs to an inferential test. Means, standard deviations and percentages describing the sample have no p value.
Sources
- 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
- 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
- Benjamin, D. J., Berger, J. O., Johannesson, M., et al. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6–10. https://doi.org/10.1038/s41562-017-0189-z
- Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567, 305–307. https://doi.org/10.1038/d41586-019-00857-9
- Lakens, D. (2017). Equivalence tests: A practical primer for t tests, correlations, and meta-analyses. Social Psychological and Personality Science, 8(4), 355–362. https://doi.org/10.1177/1948550617697177
- American Psychological Association. (2020). Publication Manual of the American Psychological Association (7th ed.). https://apastyle.apa.org/
Sources last checked: November 2026. This article is general educational guidance and does not replace your university regulations or your supervisor’s instructions. How we prepare our content.