A p-value is the probability of observing results at least as extreme as the sample results, assuming the null hypothesis is true
A small p-value suggests the observed data are unlikely under the null hypothesis
A large p-value suggests the observed data are reasonably compatible with the null hypothesis
A p-value does not measure the probability that the null hypothesis is true
A p-value does not measure the size or importance of an effect
A p-value does not prove that a result is practically significant
A p-value below a chosen significance level may be treated as evidence against the null hypothesis
A p-value above a chosen significance level means there is not enough evidence to reject the null hypothesis
A p-value should be interpreted in the context of the study design, sample size, and assumptions
A p-value is not the same as the chance that the result happened by random chance alone
A p-value depends on the test used and the data collected
A p-value should be considered alongside confidence intervals and effect sizes