Final answer:
The test statistic is a value computed from the sample data during hypothesis testing, and the p-value indicates the probability of observing a test statistic as extreme as the sample's, assuming the null hypothesis is true. A common significance level for tests is 5% (α = 0.05).
Step-by-step explanation:
In hypothesis testing, the test statistic is a standardized value computed from sample data, used to decide whether to reject the null hypothesis. The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the one calculated from the sample data, assuming the null hypothesis is true. If the p-value is less than the chosen significance level (α), usually 0.05, you reject the null hypothesis.
The process usually involves stating the null and alternative hypotheses, calculating a test statistic (like z-score, t-score, F-statistic depending on the test conditions), finding the p-value, and then comparing the p-value to the significance level to reach a conclusion.
The level of significance for a study conducted with α = 0.05 is 5%, indicating a 5% risk of rejecting the null hypothesis when it is actually true.