61 The p value

In econometrics, understanding critical values and p-values is crucial for hypothesis testing. This video explains how Z and T tables help us interpret the extremity of observed results by translating distances from zero into probabilities. We explore the concept of a p-value as the probability of observing a result as extreme or more extreme than the one obtained, assuming the null hypothesis is true. This fundamental concept helps us gauge how surprising our findings are within the framework of statistical assumptions. In this video, we clarify the distinction between a test statistic's distance from zero and its associated probability. We delve into how Z and T tables are used to convert these distances into tail areas, which represent the p-value under the null hypothesis. The video emphasizes that the p-value is not the probability of the null hypothesis being true, nor is it the probability of a result happening by chance or the size of an effect. Instead, it serves as a measure of surprise, indicating how unlikely an observed result is if the null hypothesis were valid. A large p-value suggests the result is typical under the null, while a small p-value indicates a rare outcome that the null hypothesis struggles to explain. Subscribe to @AxiomTutoringCourses for more econometrics tutorials.