1. The p-value is a concept in statistics used to determine the significance of results from a hypothesis test. 2. It represents the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. 3. The formula for the p-value depends on the test being used (e.g., z-test, t-test), but generally it is calculated from the test statistic's distribution. 4. A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. 5. A large p-value (> 0.05) suggests weak evidence against the null hypothesis, so you fail to reject it. 6. To find the p-value, you first calculate the test statistic from your sample data, then use the appropriate distribution to find the probability of observing a value as extreme or more extreme. 7. For example, in a z-test, if the test statistic is $z$, the p-value for a two-tailed test is $2 \times P(Z > |z|)$ where $Z$ is a standard normal variable. 8. Understanding p-values helps in making decisions about hypotheses based on data.