Which statistical method is commonly used for hypothesis testing?

The method commonly used for hypothesis testing is the t-tests or ANOVA. These techniques are designed specifically for comparing the means of different groups to determine if there are statistically significant differences between them.

T-tests are typically utilized when comparing the means of two groups, such as evaluating the effectiveness of a treatment by comparing outcomes of a treatment group with those of a control group. On the other hand, ANOVA (Analysis of Variance) is employed when comparing the means of three or more groups, allowing researchers to assess the impact of different factors on a single outcome.

Both t-tests and ANOVA are based on certain assumptions about the data, such as normality and homogeneity of variance, which are essential for ensuring valid results. These methods provide p-values that help determine whether to reject or fail to reject the null hypothesis, making them fundamental to the hypothesis testing process in various scientific and research settings.

In contrast, regression analysis is primarily used for modeling relationships between variables rather than for testing hypotheses about group means. Linear programming focuses on optimization problems, and descriptive statistics summarize and describe data without making inferences about populations.