Hypothesis Testing

Hypothesis testing is a statistical method used to determine whether a hypothesis is supported by the data. It is a process of making an educated guess about the value of a population parameter, and then collecting data to see if the guess is correct.

The first step in hypothesis testing is to state the null hypothesis and the alternative hypothesis. The null hypothesis is the hypothesis that there is no difference between the two populations. The alternative hypothesis is the hypothesis that there is a difference between the two populations.

The next step is to choose a significance level. The significance level is the probability of making a type I error, which is rejecting the null hypothesis when it is true. The most common significance level is 0.05.

The third step is to choose a test statistic. The test statistic is a measure of the difference between the two populations. The most common test statistic is the t-statistic.

The fourth step is to calculate the p-value. The p-value is the probability of getting a test statistic as extreme as the one observed, if the null hypothesis is true.

The fifth step is to make a decision. If the p-value is less than the significance level, then the null hypothesis is rejected. If the p-value is greater than the significance level, then the null hypothesis is not rejected.

Hypothesis testing is a powerful tool for making inferences about the population. However, it is important to remember that hypothesis testing is not perfect. There is always a chance of making a type I or type II error.