Failing to detect a genuine effect when it exists is called what?

• A. Type II error
• B. Type I error
• C. Systematic error
• D. Random error

Answer: (A)

Failing to detect a genuine effect that exists is called a Type II error, a false negative. In hypothesis testing you set up a null hypothesis that there is no effect. If there truly is an effect but your data don’t provide enough evidence to reject that null, you miss the real effect. This is different from a Type I error, where you conclude there is an effect when there isn’t one. Systematic error refers to bias that consistently distorts results, and random error is the unpredictable variability from chance. Type II error tends to happen when the study lacks enough power—small sample size, small true effect, high variability, or a strict significance level. For example, a treatment might truly help, but a small trial doesn’t show a significant difference, leading you to overlook the real effect.