Hypothesis testing is a statistical technique used to evaluate the likelihood of a claim or hypothesis about a population parameter based on a sample of data. It involves formulating a null hypothesis (H0), which is a statement of no difference or no effect, and an alternative hypothesis (Ha), which is a statement of a difference or an effect. The null hypothesis is tested against the alternative hypothesis to determine whether there is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis.
To conduct hypothesis testing, a researcher first formulates a null hypothesis and an alternative hypothesis, and selects a significance level, typically denoted by α, which represents the maximum probability of rejecting the null hypothesis when it is actually true (Type I error). The researcher then collects data and calculates a test statistic, which measures the degree of difference between the sample data and the null hypothesis. The test statistic is compared to a critical value, which is determined by the significance level and the degrees of freedom associated with the test. If the test statistic is greater than the critical value, the null hypothesis is rejected in favor of the alternative hypothesis, indicating that there is sufficient evidence to support the claim of a difference or an effect.
Hypothesis testing is widely used in scientific research, business, and many other fields to make decisions based on data. It is used to test whether a new treatment or product is more effective than an existing one, to evaluate the impact of a policy or program, to test whether a relationship exists between two variables, or to determine whether a sample of data is representative of a population.