There is an inherent inverse relationship between the probability of committing a Type I error and a Type II error. If researchers attempt to reduce Type I errors by setting a stricter alpha level (e.g., $.01$), they make it harder to reject true null hypotheses, but they inadvertently make it harder to reject false ones, thereby increasing the risk of Type II errors. Conversely, raising the alpha level (e.g., $.10$) reduces Type II errors but increases Type I errors. The standard alpha level of $.05$ serves as a conventional balance to keep both error rates acceptable.