In hypothesis testing, which of the following best describes the p-value?

The p-value quantifies how likely it is to observe the sample data (or more extreme results) under the assumption that the null hypothesis is correct. A low p-value (e.g., <0.05) suggests that the observed data is unlikely under the null hypothesis, leading to its rejection in favor of the alternative hypothesis. For example, if testing a new drug, a p-value of 0.03 indicates a 3% chance that the observed effect is due to random variation under the null hypothesis. Why Other Options Are Wrong : A) The p-value doesn’t measure the truth of the null hypothesis itself. C) This describes the power of a test, not the p-value. D) The threshold (e.g., 0.05) is set by the researcher; it’s not the p-value. E) This describes R-squared in regression analysis, not hypothesis testing.