Statistical power correct rejection – UGC NET Question

The power of a statistical test is the probability that it will detect an effect and reject the null hypothesis when the null is actually false. High power means a lower chance of committing a Type II error, or false negative. Power is influenced by factors such as sample size, effect size, variability in the data and the chosen significance level. Because the stem describes the probability of correctly rejecting a false null hypothesis, it is defining the power of the test.

Option A:
Significance level, often denoted by alpha, is the probability of committing a Type I error—that is, rejecting a true null hypothesis. It sets the threshold for how much risk of a false positive the researcher is willing to accept. Since the stem is about correctly rejecting a false null, significance level is not the correct answer.

Option B:
Effect size quantifies the magnitude of a relationship or difference, providing information about practical importance rather than decision error probabilities. While larger effect sizes generally increase power, effect size itself is not the probability of correct rejection. Thus, effect size does not fit the description in the question.

Option C:
Confidence level refers to the proportion of times that confidence intervals constructed from repeated samples would contain the true population parameter. It is related to estimation rather than hypothesis testing power. Therefore, confidence level is not the term that describes the probability of correctly rejecting a false null hypothesis.

Option D:
Power reflects the sensitivity of a test to detect real effects, guiding researchers in planning sample sizes to avoid underpowered studies that may miss meaningful results. Given that the stem explicitly refers to correctly rejecting a false null hypothesis, power is the appropriate completion.