Which of the following is a key difference between Type I and Type II errors?

A key distinction between Type I and Type II errors lies in their definitions and implications. A Type I error occurs when a researcher rejects a null hypothesis that is actually true, leading to a false positive conclusion—essentially claiming that there is an effect or difference when there is none. On the other hand, a Type II error occurs when a researcher fails to reject a null hypothesis that is actually false, resulting in a false negative conclusion—indicating that there is no effect or difference when, in fact, there is one. This clear differentiation highlights that Type I errors relate to incorrectly identifying something as significant when it is not, while Type II errors are about missing a significant finding that exists.

The other options do not accurately capture this fundamental characteristic. For example, the frequency of errors (as mentioned in one of the choices) can vary based on the significance level set by the researcher and the statistical power of the test, but this does not define the types of errors themselves. The discussion of hypothesis acceptance or rejection also mischaracterizes the errors since both errors are associated with decisions made regarding the null hypothesis. While sample size may influence the likelihood of these errors, it does not define the nature of the errors themselves.