Type I error false rejection – UGC NET Question

A Type I error occurs when a statistical test leads the researcher to reject the null hypothesis even though it is actually true in the population. This is also called an alpha error and represents a false positive conclusion. The probability of committing a Type I error is controlled by the chosen significance level, such as 0.05. Hence, the situation described in the stem is correctly termed a Type I error.

Option A:
A Type II error happens when the test fails to reject the null hypothesis even though it is false, leading to a false negative conclusion. This concerns lack of power rather than over-detection of effects, so it does not match the definition in the question.

Option B:
Sampling error refers to natural fluctuations between sample statistics and population parameters due to observing only a subset of the population. It is a broader concept and is not specifically about the incorrect rejection of a true null hypothesis. Therefore, sampling error is not the correct completion.

Option C:
Systematic error is a consistent bias in measurement or procedure that skews results in one direction, often threatening validity, but it is not the specific decision error defined in statistical hypothesis testing. Thus, systematic error is not appropriate here.

Option D:
Type I errors are particularly serious in contexts where wrongly claiming an effect can lead to unnecessary or harmful interventions, which is why researchers often set conservative significance levels. This role fits exactly with the stem’s description of rejecting a true null hypothesis.