What is a Type II error?

• A. False positive
• B. True positive
• C. False negative
• D. True negative

Answer: (C)

A Type II error is when you miss a real effect by not rejecting the null hypothesis. In other words, there is actually a true effect, but your test concludes there isn’t one. For example, a new treatment may genuinely help, but your study ends up saying it doesn’t work. This is a false negative: you fail to detect an actual difference. By contrast, a false positive would claim there is an effect when there isn’t one, a true positive would correctly detect an actual effect, and a true negative would correctly show no effect when there isn’t one. So the description that fits a Type II error is missing a real effect and concluding there isn’t one.