False Negative

A false negative, also known as Type II or beta error, is an error that occurs when a researcher falsely concludes that there is no effect. Therefore, this is also sometimes referred to as falsely failing [Page 540]to reject the null hypothesis (or simply “falsely failing to reject the null”). It is important to understand the concept of a false negative when conducting communication research so that scholars do not erroneously claim that an effect does not exist and so that scholars do not miss important findings. For example, assume that the true state of the world (reality) is that women have higher emotional intelligence scores than do men. A researcher collects data from a sample of men and women and finds that for this sample, men and women have similar emotional intelligence scores. On the basis of these data, the researcher concludes that there is no difference between men and women on emotional intelligence. Provided that in reality, there is a difference between men and women on emotional intelligence, the researcher has committed an error. This error is a false negative (or a Type II or beta error) because the researcher failed to detect an effect that actually exists. This entry examines the relationship between a false negative and statistical power, discusses the various ways that Type II error can be minimized, and concludes with an example relevant to communication research.