What term describes the probability of obtaining data as extreme as, or more extreme than, the actual observed data under the null hypothesis?

The term that describes the probability of obtaining data as extreme as, or more extreme than, the actual observed data under the null hypothesis is the p-value. The p-value serves as a statistical measure that helps determine the significance of the results obtained from a hypothesis test.

In hypothesis testing, the null hypothesis represents a statement of no effect or no difference, and researchers use the p-value to decide whether to reject this null hypothesis. If the p-value is less than a predetermined significance level (often set at 0.05), it indicates that the observed data would be unlikely under the null hypothesis, leading researchers to reject the null in favor of the alternative hypothesis.

Confidence intervals provide a range of values within which the true parameter is expected to lie, but they do not directly measure the probability of obtaining extreme data under the null hypothesis. The standard error is a statistic that measures the accuracy with which a sample represents a population, but it does not provide an assessment of extreme values as related to the null hypothesis. The significance level is a predetermined threshold for determining whether to reject the null hypothesis, generally denoted as alpha, which sets the criteria for the p-value but is not the same as the p-value itself.