Which statement about p-values is true?

• A. It is the probability that the null hypothesis is true
• B. It is the probability of observing data as extreme as what was observed, assuming the null is true
• C. It proves the alternative hypothesis
• D. It indicates the effect size

Answer: (B)

A p-value measures how compatible the observed data are with the idea that there is no real effect. It is the probability, under the null hypothesis, of obtaining data at least as extreme as what was actually observed. This means a small p-value signals that the observed result would be unlikely if the null were true, leading you to consider rejecting the null at a chosen significance level. It does not tell you the probability that the null is true, nor does it prove the alternative hypothesis, and it does not indicate how large the effect is. The size of the p-value can also be influenced by sample size: large samples can yield small p-values for trivial effects, while small samples may not detect meaningful effects.