Psychology Qualifying Practice Exam

Two main ideas in hypothesis testing are the alpha level and the p-value. The alpha level is a pre-set significance threshold you choose before collecting data; it represents the maximum chance you’re willing to accept of making a Type I error—concluding there is an effect when there isn’t one. The p-value, calculated from your data, tells you how likely you would see data as extreme as what you observed if the null hypothesis were true. If that p-value is smaller than your alpha, you declare the result statistically significant and reject the null. If it isn’t, you don’t reject the null. For example, with an alpha of 0.05, a p-value of 0.03 leads to rejection, while a p-value of 0.08 would not. Remember, the p-value isn’t the probability that the null hypothesis is true, but the probability of observing your data (or more extreme) under the assumption that the null is true.