The p-value is one of the most common statistical outputs and one of the most frequently misinterpreted. A careful interpretation produces more accurate research conclusions.
What does a p-value mean?
It is the probability of observing a result at least as extreme as the one obtained, assuming the null hypothesis and the test assumptions are true. It is not the probability that the null hypothesis is true.
Significance is not importance
A tiny effect may be statistically significant in a large sample, while a practically relevant effect may not reach significance in a small sample. Read the effect size and confidence interval alongside p.
Avoid saying there is no relationship
When p is above the chosen threshold, the precise conclusion is that the data did not provide enough evidence to reject the null hypothesis. A nonsignificant result does not prove that an effect is absent.
Set alpha in advance
Choose a threshold such as .05 before examining the results. If many tests are performed, consider whether an adjustment for multiple comparisons is needed.
An example reporting sentence
A result may be reported as: the groups differed, t(86)=2.18, p=.032, followed by an effect size and confidence interval. This gives readers more information than a significant or nonsignificant label.
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