Researchers retain or reject hypothesis based on measurements of observed samples. The decision is often based on a statistical mechanism called hypothesis testing. A type I error is the mishap of falsely rejecting a null hypothesis when the null hypothesis is true. The probability of committing a type I error is called the significance level of the hypothesis testing, and is denoted by the Greek letter α .
In the following tutorials, we demonstrate the procedure of hypothesis testing in R first with the intuitive critical value approach. Then we discuss the popular p-value approach as alternative.