Logic of Hypothesis Testing: The first step in hypothesis testing is to understand the logic behind it. You start with a null hypothesis (H0), which represents a default assumption, and an alternative hypothesis (Ha), which represents what you want to test. For example, H0: μ = 100 (population mean is 100) vs. Ha: μ ≠ 100 (population mean is not 100).
Choosing the Significance Level (Alpha): The significance level, denoted as alpha (α), is the probability of making a Type I error (incorrectly rejecting a true null hypothesis). Common values for alpha are 0.05 or 0.01. You can set alpha using alpha <- 0.05.
Performing Hypothesis Tests: R provides various functions for hypothesis testing, such as t.test() for t-tests and chisq.test() for chi-square tests. For a two-sample t-test, you can use:
t_test_result <- t.test(x, y, alternative = "two.sided")
