In hypothesis testing, what does a p-value indicate?

• A. The probability that the null hypothesis is false
• B. The probability of observing the data if the null hypothesis is true
• C. The probability that the alternative hypothesis is true
• D. The confidence level of the test

Answer: (B)

In hypothesis testing, the p-value reflects how compatible the observed data are with the assumption that the null hypothesis is true. It is the probability, under that assumption, of obtaining data at least as extreme as what was observed (for a two-tailed test, extreme in either direction). A small p-value means the data would be unlikely if the null were true, which is why we might reject the null at our chosen significance level. It does not measure the probability that the null is true or false, nor the probability that the alternative is true, and it’s not the test’s confidence level.