What statistical error occurs when a true difference exists but the study fails to detect it?

What statistical error occurs when a true difference exists but the study fails to detect it?

• A. Type I error
• B. Type II error
• C. Regression toward the mean
• D. Power

Answer: (B)

Type II error occurs when a true difference exists but the study fails to detect it. In other words, you conclude there is no difference even though there actually is one. This can happen when the sample size is too small, variability is high, or the true effect is modest. It’s a false negative, meaning you miss a real effect. Power is the probability of detecting a true difference if it exists, so low power increases the likelihood of a Type II error, while higher power reduces it. By contrast, a Type I error would be detecting a difference when none exists (a false positive). Regression toward the mean describes a natural tendency for extreme values to move closer to the average on repeated measurements, not an error in detecting a true difference.

Type II error occurs when a true difference exists but the study fails to detect it. In other words, you conclude there is no difference even though there actually is one. This can happen when the sample size is too small, variability is high, or the true effect is modest. It’s a false negative, meaning you miss a real effect. Power is the probability of detecting a true difference if it exists, so low power increases the likelihood of a Type II error, while higher power reduces it. By contrast, a Type I error would be detecting a difference when none exists (a false positive). Regression toward the mean describes a natural tendency for extreme values to move closer to the average on repeated measurements, not an error in detecting a true difference.