Which statement about p-values in hypothesis testing is true?

Which statement about p-values in hypothesis testing is true?

• A. A smaller p-value indicates less evidence against the null hypothesis.
• B. A smaller p-value indicates greater evidence against the null hypothesis.
• C. P-values measure the probability that the alternative is true.
• D. P-values are independent of the data.

Answer: (B)

Interpreting p-values relies on understanding what they measure. A p-value is the probability, assuming the null hypothesis is true, of obtaining data as extreme or more extreme than what was actually observed. A smaller p-value means the observed result is unlikely under the null, which provides stronger evidence against the null hypothesis. That’s why the statement that a smaller p-value indicates greater evidence against the null is the true one. P-values do not tell us the probability that the alternative hypothesis is true, and they depend on the data you collected. They are calculated from the observed data and the assumed distribution under the null, so a different sample can yield a different p-value.

Interpreting p-values relies on understanding what they measure. A p-value is the probability, assuming the null hypothesis is true, of obtaining data as extreme or more extreme than what was actually observed. A smaller p-value means the observed result is unlikely under the null, which provides stronger evidence against the null hypothesis. That’s why the statement that a smaller p-value indicates greater evidence against the null is the true one.

P-values do not tell us the probability that the alternative hypothesis is true, and they depend on the data you collected. They are calculated from the observed data and the assumed distribution under the null, so a different sample can yield a different p-value.