Which of the following statements best describes the p-value in statistical hypothesis testing?
AThe probability of failing to reject the null hypothesis, given the observed results.
BThe probability that the null hypothesis is true, given the observed results.
CThe probability that the observed results are statistically significant, given that the null hypothesis is true.
DThe probability of observing results as extreme or more extreme than currently observed, given that the null hypothesis is true. ✓ Correct
Correct answer: (D) The probability of observing results as extreme or more extreme than currently observed, given that the null hypothesis is true.
Explanation
The p-value is the probability of obtaining results as extreme or more extreme than those observed, assuming the null hypothesis is true.
It is not the probability of failing to reject the null, which confuses the p-value with a decision outcome.
It is not the probability that the null hypothesis is itself true, a common misinterpretation.
It is not the probability that results are significant given the null, which reverses the logic.
A small p-value signals that the observed data would be unlikely under the null hypothesis.
Ronald Fisher popularised the 0.05 threshold as a conventional cut-off for significance.
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