What is a Type II error?

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

Answer: (B)

A Type II error is when there really is an effect or difference (the null hypothesis is false), but your test fails to detect it and you fail to reject the null. In other words, you miss a real effect. This happens when the study doesn’t have enough power, which can come from a small sample size, a subtle effect, or a lot of variability in the data. For example, if a new treatment actually improves outcomes but the study doesn’t show a statistically significant difference, you’ve made a Type II error. To reduce this risk, researchers try to increase power by increasing sample size, reducing measurement noise, or adjusting design and analysis to be more sensitive. The other kinds of errors involve rejecting a true null (Type I error) or making the correct decision when the null is true or false (rejecting a false null is a correct decision, while failing to reject a true null is also correct).