Define a Type I error.

• A. Failing to reject a true null hypothesis
• B. Failing to reject a false null hypothesis
• C. Rejecting a false null hypothesis
• D. Rejecting a true null hypothesis

Answer: (D)

A Type I error is when you reject a null hypothesis even though it is actually true. This is a false-positive decision: you conclude there is an effect or difference when none exists. The chance of making this error is set by the test’s significance level (alpha), which controls how often you’re willing to reject a true null because of random variation in the data.

Think of a scenario where the null says a drug has no effect. If the study results show a statistically significant effect and you conclude the drug works, but in reality it doesn’t, that’s a Type I error.

The other descriptions refer to different situations: not rejecting a true null is a correct decision (not an error); not rejecting a false null is a Type II error (missing a real effect); and rejecting a false null is a correct decision with sufficient power.