To calculate the p-value in hypothesis testing, first determine the test statistic based on your sample data, using the appropriate method for the type of test you are conducting. Next, consult the relevant statistical distribution to find the probability of observing a test statistic as extreme as the one calculated, and compare the p-value to your predetermined significance level (alpha) to decide whether to reject or fail to reject the null hypothesis.

To calculate the p-value in hypothesis testing, first, state the **null hypothesis (H0)** and **alternative hypothesis (H1)**, and choose a significance level (**α**, commonly 0.05). Collect sample data and select an appropriate statistical test (e.g., **t-test, z-test**). Calculate the **test statistic** using the relevant formula. Then, determine the p-value by using statistical software or tables that correspond to the calculated test statistic. Finally, compare the p-value to α: if the **p-value ≤ α**, reject H0, indicating evidence for H1; if **p-value > α**, do not reject H0, suggesting insufficient evidence. The p-value thus quantifies the probability of observing the results under the assumption that H0 is true.

To calculate the p-value in hypothesis testing, first perform the appropriate statistical test to obtain a test statistic. Then, using the distribution associated with that test (e.g., t-distribution or normal distribution), determine the probability of observing a value as extreme or more extreme than the test statistic, assuming the null hypothesis is true. This probability is the p-value.