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Understanding P-Values: P-values are essential in hypothesis testing. They quantify the strength of evidence against the null hypothesis. Smaller p-values indicate stronger evidence against the null. In R, p-values are typically calculated and returned by hypothesis testing functions.
Interpreting P-Values: If your p-value is less than alpha (α), you reject the null hypothesis. For example, if p < 0.05 (with α = 0.05), you have evidence to reject H0. If p > α, you fail to reject H0. Keep in mind that p-values do not prove a null hypothesis; they provide evidence for or against it.
By following these steps and using R's built-in functions for hypothesis testing, confidence intervals, and p-value calculations, you can unlock the secrets of inferential statistics. This allows you to make data-driven decisions, draw meaningful conclusions, and test hypotheses based on your data analysis in R.
