The three types of t-test are independent-samples t-test, a one-sample t-test, and a paired sample t-test. The independent-sample t-test compares the mean of two independent groups, to determine if there is any statistical association based on the same variable (Corty, 2016). For example, if a teacher wanted to see if intellect is related to gender, she would give a test to all the students in the school and compare the mean for the test results based on the genders. This example is appropriate because there are two groups male and female and the variable is test scores.

A paired sample t-test also known as a dependent sample t-test compares the means of two dependent samples on the same subject/object (Corty, 2016). For example, if a researcher wanted to test the effect of testosterone pills on males before and after training by measures of a blood test. This example is appropriate because the two dependents are before and after training and the subject/object is males.

A single sample t-test compares a sample mean to a population mean when the population standard deviation is not known. Additionally, the test is used to analyze any significant differences between a population mean and the hypothesized value (Corty, 2016). For example, the population means for male’s height is 5.6 and a researcher wants to see if the average height of males from a specific college is 5.9.

Reference

Corty, E. (2016). Using and interpreting statistics: A practical text for the behavioral, social, and health sciences (3rd ed.). New York, NY: Macmillan Learning.

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