Example Concept: A common pitfall is misinterpreting the p-value as the probability that the null hypothesis is true. Instead, the p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. Another mistake is using a p-value threshold (e.g., 0.05) as a definitive proof of effect or no effect, without considering the context, effect size, or study design. Additionally, p-values do not measure the size or importance of an effect, and failing to account for multiple comparisons can inflate Type I error rates.