Type II error false negative – UGC NET Question

A Type II error occurs when the null hypothesis is actually false but the statistical test does not detect sufficient evidence to reject it. This results in a false negative conclusion, where a real effect or difference is overlooked. The probability of a Type II error is denoted by beta, and its complement is the power of the test. Thus, the situation described in the stem is correctly named a Type II error.

Option A:
Type II errors are more likely when sample sizes are small, effect sizes are weak or variability in the data is high. Researchers try to control this risk by planning adequate sample sizes and using sensitive measures. Because the stem focuses on failing to reject a false null, this option is appropriate and correct.

Option B:
Measurement error refers to inaccuracies in how variables are assessed and can contribute to both Type I and Type II errors but does not specifically name the hypothesis decision error.

Option C:
Sampling error arises from studying a subset rather than the entire population and may contribute to uncertainty but is not the particular decision error of missing a true effect.

Option D:
Computational errors involve mistakes in calculation or coding and can lead to any kind of incorrect conclusion; they are not the conceptual category of error defined in statistical theory as Type II.