Independent Samples t-test:
It is also known as a two-sample t-test, this statistical method is employed to ascertain if there is a significant difference between the means of two separate and unrelated groups. This test assumes that both groups are independent and exhibit a normal distribution, with roughly equivalent variances.
Paired Samples t-test:
It is also known as a dependent samples t-test or a paired t-test, this analytical tool is applied when dealing with two sets of interrelated or paired data points. Its purpose is to evaluate whether the means of the discrepancies between paired observations are significantly distinct from zero. Typically, this test is utilized in scenarios such as before-and-after studies or when comparing two related treatments or conditions.
One-Sample t-test:
This statistical technique is utilized to compare the mean of a single sample against a known or hypothesized population mean. Its objective is to determine whether the sample mean significantly differs from the hypothesized population mean.
Welch’s t-test:
This method resembles the independent samples t-test; however, it does not make the assumption of equal variances between the two groups. It proves valuable when the assumption of equal variances is violated, and there is an unequal sample size or variance between the two groups.
Student’s t-test for Equal Variance (Pooled t-test):
A variant of the independent samples t-test, this test operates under the assumption of equal variances in the two groups. It is chosen when the assumption of equal variances is reasonable and holds true.
Student’s t-test for Unequal Variance (Unpooled t-test):
Much like Welch’s t-test, this test is employed when dealing with two independent samples, but without the assumption of equal variances between the groups. It is also known as the Behrens-Fisher problem.
Two-Sample t-test for Proportions:
This statistical approach is used to compare the proportions of two unrelated groups. It finds application in scenarios where there is an interest in comparing the success rates or proportions between the two groups.