Type I error false rejection – UGC NET Question

A Type I error occurs when the null hypothesis is actually true but the sample data lead the researcher to reject it, suggesting an effect that does not exist in the population. It is also called an alpha error or false positive. The chosen significance level, such as 0.05, represents the maximum probability of committing this kind of error under repeated sampling. Therefore, incorrectly rejecting a true null hypothesis is known as a Type I error.

Option A:
Option A clearly labels this error as Type I, which is central to decisions about the significance level and interpretation of p-values. Researchers aim to keep the probability of such false alarms low, consistent with the description in the stem.

Option B:
A Type II error, by contrast, occurs when a false null hypothesis is not rejected, leading to a failure to detect a real effect; this is a false negative situation, not the false positive described. Hence, Type II is not the correct answer.

Option C:
Sampling error refers more broadly to the natural differences between sample statistics and population parameters due to using a subset of the population. While related to hypothesis testing, it is not the specific decision error of rejecting a true null.

Option D:
Measurement error involves inaccuracies in data due to instrument flaws or respondent factors and is different from the inferential decision errors in hypothesis testing, so it is not appropriate here.