Which error type is associated with incorrectly accepting a false null hypothesis?

Which error type is associated with incorrectly accepting a false null hypothesis?

• A. Type I Error
• B. Type II Error
• C. Type III Error
• D. Type IV Error

Answer: (B)

The correct choice refers to a Type II Error, which occurs when a researcher fails to reject a false null hypothesis. In hypothesis testing, the null hypothesis typically proposes that there is no effect or no difference, and researchers conduct tests to determine whether there is enough evidence to reject this hypothesis in favor of an alternative hypothesis. When a Type II Error takes place, it means that the statistical test did not detect an effect or difference that actually exists. This can lead to the misleading conclusion that there is no significant effect or relationship when, in fact, one is present. Such errors are critical to consider, as they can impact the validity of research findings and lead to missed opportunities for discovering meaningful results. Understanding Type II Error is fundamental in evaluating the effectiveness of research designs and in selecting sample sizes, as larger sample sizes can reduce the risk of committing such an error, thereby increasing statistical power to detect true effects.

The correct choice refers to a Type II Error, which occurs when a researcher fails to reject a false null hypothesis. In hypothesis testing, the null hypothesis typically proposes that there is no effect or no difference, and researchers conduct tests to determine whether there is enough evidence to reject this hypothesis in favor of an alternative hypothesis.

When a Type II Error takes place, it means that the statistical test did not detect an effect or difference that actually exists. This can lead to the misleading conclusion that there is no significant effect or relationship when, in fact, one is present. Such errors are critical to consider, as they can impact the validity of research findings and lead to missed opportunities for discovering meaningful results.

Understanding Type II Error is fundamental in evaluating the effectiveness of research designs and in selecting sample sizes, as larger sample sizes can reduce the risk of committing such an error, thereby increasing statistical power to detect true effects.