Almost every quantitative thesis reports p-values — and a surprising number misinterpret them. Getting this right protects you in your viva and keeps your conclusions honest, because 'significant' does not mean what many scholars think it means.
What a p-value actually is
A p-value is the probability of observing results at least as extreme as yours if the null hypothesis were true. A small p-value (commonly < 0.05) suggests your data would be unlikely under 'no effect', so you reject the null. That's it — it is not the probability that your hypothesis is true.
What it does NOT tell you
- It is not the probability that your hypothesis is correct.
- It does not measure the size or importance of an effect.
- A 'significant' result can still be trivially small in practice.
- A 'non-significant' result does not prove there is no effect.
Significance tells you whether an effect is likely real; effect size tells you whether it matters. Always report effect sizes (and confidence intervals) alongside p-values — examiners and reviewers increasingly insist on it.
Use it responsibly
Don't 'p-hack' — running endless tests until something dips below 0.05 is misconduct dressed as analysis, and it's fragile. Set your hypotheses first, and remember that adequate sample size shapes what your p-values can tell you. For running and interpreting tests correctly, see SPSS mentoring.
Frequently asked
What does p < 0.05 mean?+
It means that if there were truly no effect (the null hypothesis), you'd see data this extreme less than 5% of the time — so the result is treated as statistically significant. It does not mean there's a 95% chance your hypothesis is true.
Is a non-significant result useless?+
No. Failing to find an effect can be informative, especially in a well-powered study, and honest null results matter to the field. It simply means your data didn't provide enough evidence to reject the null — not that no effect exists.
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