Final answer:
Behavioral researchers commonly set the alpha-level at 0.05, indicating a 5 percent chance of making a Type I error, which is rejecting a true null hypothesis.
Step-by-step explanation:
Typically, behavioral researchers set the alpha-level such that they will make a Type I error 5 percent of the time. The alpha-level, denoted as α, represents the probability of rejecting the null hypothesis when it is actually true. This threshold is commonly set at 0.05, meaning there is a 5 percent chance of making a Type I error in hypothesis testing. It's like a safety net to control the rate of false positives, where researchers falsely claim an effect or difference when none exists.
A Type I error can be contextualized with examples such as concluding that the proportion of women who develop breast cancer is at least 11 percent when it is really less than 11 percent, or asserting that more than 60 percent of Americans vote in presidential elections when the actual percentage is at most 60 percent. In contrast, a Type II error occurs when one fails to reject a false null hypothesis; for example, accepting that at most 60 percent of Americans vote in presidential elections when more than 60 percent actually do. The probability of a Type II error is denoted by the symbol B.
The alpha level dictates the stringency of the hypothesis test. A lower alpha level (e.g., α = 0.01) indicates a more conservative test that is less likely to make a Type I error but may increase the chance of a Type II error. Researchers must balance these risks when choosing an alpha level, especially in contexts where the consequence of a Type I error is severe, such as in medical trials or policy-making decisions.