Define type I (alpha) and type II (beta) errors.

• A. Type I: false positive; Type II: false negative.
• B. Type I: false negative; Type II: false positive.
• C. Type I: sampling bias; Type II: measurement bias.
• D. Type I: when outcomes are the same; Type II: when outcomes differ.

Answer: (A)

In hypothesis testing, you decide whether to reject the null hypothesis based on sample data. A Type I error happens when you reject a true null hypothesis—that’s a false positive: you conclude there is an effect or difference when there isn’t one. The probability of this error is the significance level, alpha.

A Type II error occurs when you fail to reject a false null hypothesis—that’s a false negative: there is a real effect or difference, but your test misses it. The probability of this error is beta, and the test’s power is 1 minus beta.

In practice, lowering alpha reduces the chance of a false positive but can increase the chance of a false negative unless you increase the sample size. For example, in a trial for a new drug, a Type I error would be concluding the drug works when it doesn’t; a Type II error would be concluding it doesn’t work when it actually does.