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* **P-value:** This is the probability of observing the data (or more extreme data) if the null hypothesis is true. It tells you how likely the results you obtained would be if the null hypothesis were actually correct. A small p-value (typically less than the significance level) suggests that the data are unlikely if the null hypothesis is true, leading us to reject the null hypothesis. This means we have enough evidence to support the alternative hypothesis. A large p-value (greater than the significance level) suggests that the data are consistent with the null hypothesis, so we fail to reject the null hypothesis. The image of the hypothesis visually represents how these two concepts interact with data and how we make decisions based on the analysis.
