Rejecting the null hypothesis when it is true is called what?

• A. Type I error
• B. Type II error
• C. Sampling error
• D. Measurement error

Answer: (A)

The situation described is a Type I error, also known as a false positive. It occurs when you conclude there is an effect or difference and reject the null hypothesis, even though the null is actually true. This happens because of random variation in the data; with a chosen significance level (alpha), there’s a built-in risk of this happening. For example, setting alpha at 0.05 means you’re willing to accept a 5% chance of making this error over many repetitions of the study.

It’s useful to contrast with Type II error, which is failing to reject a false null hypothesis (a missed real effect). Sampling error refers to natural fluctuations between a sample statistic and the true population value due to random sampling, while measurement error comes from inaccuracies in how data are collected.