Type II error false acceptance – UGC NET Question

A Type II error occurs when the null hypothesis is actually false but the data do not provide enough evidence to reject it, leading the researcher to conclude that no effect exists when in fact there is one. This is known as a false negative or beta error. The probability of avoiding a Type II error is called statistical power. Thus, failing to detect a real effect corresponds to a Type II error.

Option A:
Type I error refers to rejecting a true null hypothesis, which is the opposite scenario of a false positive rather than a missed effect. Therefore, it does not fit the stem.

Option B:
Option B, Type II error, captures the idea that an inadequate sample size or high variability can make real differences appear non-significant. Researchers often conduct power analysis to minimise this risk, aligning with the description given in the question.

Option C:
Sampling error is a general term for discrepancies between sample and population values and does not specify the decision outcome in hypothesis testing. It is not equivalent to the failure to reject a false null.

Option D:
Response error occurs when respondents provide inaccurate or misleading answers, contributing to measurement problems rather than statistical testing decisions, so it is not the correct term here.