What is the p-value in hypothesis testing?

What is the p-value in hypothesis testing?

• A. The probability of obtaining the observed data or more extreme if the null hypothesis is true.
• B. The probability that the null hypothesis is true.
• C. The significance level used to decide whether to reject the null.
• D. The probability of making a Type I error.

Answer: (A)

The p-value measures how surprising the observed data would be if the null hypothesis were true. It is the probability, under the assumption that the null is true, of obtaining results as extreme as or more extreme than what was actually observed. In a two-sided test, “as extreme” means data that deviate from the null value in either direction by at least the same amount. A small p-value indicates that such data are unlikely under the null, which is why we might reject the null at a pre-set significance level. This value is not the probability that the null hypothesis is true, nor is it the probability of making a Type I error in this single study. It’s also not the pre-chosen significance level itself, which is simply the cutoff used to decide whether to reject the null.

The p-value measures how surprising the observed data would be if the null hypothesis were true. It is the probability, under the assumption that the null is true, of obtaining results as extreme as or more extreme than what was actually observed. In a two-sided test, “as extreme” means data that deviate from the null value in either direction by at least the same amount. A small p-value indicates that such data are unlikely under the null, which is why we might reject the null at a pre-set significance level.

This value is not the probability that the null hypothesis is true, nor is it the probability of making a Type I error in this single study. It’s also not the pre-chosen significance level itself, which is simply the cutoff used to decide whether to reject the null.