What is the difference between Type I and Type II errors in hypothesis testing?

• A. A false positive (concluding an effect exists when it does not).
• B. A false negative (failing to detect a true effect).
• C. Type I error is a false positive; Type II error is a false negative.
• D. Both errors refer to making correct conclusions about the null.

Answer: (C)

In hypothesis testing, there are two common mistakes you can make. A Type I error is a false positive: you conclude there is an effect or difference when there really isn’t one. A Type II error is a false negative: you fail to detect an effect or difference that actually exists. So the best description ties these two ideas together: a Type I error equals a false positive, and a Type II error equals a false negative.

Think of it in terms of the null hypothesis being true or false. If the null is true and you reject it, that’s a Type I error. If the null is false and you fail to reject it, that’s a Type II error. The other options only describe one part of the idea or imply something incorrect about the conclusions, which is why they’re not the right match. In practice, researchers manage these risks by setting the significance level (to control Type I) and by designing studies with enough power (to reduce Type II).