Which statement best describes a p-value in hypothesis testing?

Which statement best describes a p-value in hypothesis testing?

• A. The probability, under the null, of obtaining a test statistic as extreme or more extreme than observed; small p-values provide evidence against H0.
• B. The probability that the null hypothesis is true.
• C. The probability of observing data under the alternative hypothesis as extreme as the sample.
• D. The probability, under the null, of obtaining a test statistic as extreme or more extreme than observed; small p-values provide evidence against H0.

Answer: (D)

In hypothesis testing, the p-value is the probability, assuming the null hypothesis is true, of observing a test statistic as extreme or more extreme than what was actually observed. A small p-value means the data are unlikely under the null, which provides evidence against the null hypothesis and leads you to consider rejecting it at your chosen significance level. It does not tell you the probability that the null is true, nor does it describe the likelihood of the data under the alternative hypothesis.

In hypothesis testing, the p-value is the probability, assuming the null hypothesis is true, of observing a test statistic as extreme or more extreme than what was actually observed. A small p-value means the data are unlikely under the null, which provides evidence against the null hypothesis and leads you to consider rejecting it at your chosen significance level. It does not tell you the probability that the null is true, nor does it describe the likelihood of the data under the alternative hypothesis.