Which of the following describes Type I and Type II errors?

• A. Type I error is a false positive; Type II error is a false negative.
• B. Type I error is a false negative; Type II error is a false positive.
• C. Type I error relates to study design bias; Type II error relates to data collection error.
• D. Type I and II errors are both about sampling size.

Answer: (A)

In hypothesis testing, there are two common decision errors. A Type I error is a false positive: you conclude there is an effect or association when there isn’t one. A Type II error is a false negative: you fail to detect an effect or association when there really is one. This makes the statement that Type I is a false positive and Type II is a false negative the best description. The other choices misstate the definitions, or misattribute the errors to bias, data collection issues, or sampling size rather than the decision process in hypothesis testing.