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 Independent samples in hypothesis testing are used to measure information and data of individual unknown populations or treatments (Gravetter, Wallnau, Forzano & Witnauer, 2021). In both, a one-independent and two-independent samples t-test, the basic structure is the same, measuring the actual difference of sample data and hypothesis, over the expected difference of sample data and hypothesis that has no treatment effect. Uing the numerator to measure what the difference is, “The single-sample t uses one sample mean to test a hypothesis about one population mean” (Gravetter, Wallnau, Forzano & Witnauer, 2021, Pg. 327). “The independent-measures t uses the difference between two sample means to evaluate a hypothesis about the difference between two population means” (Gravetter, Wallnau, Forzano & Witnauer, 2021, Pg. 328). A situation where a research study involving a two-interdependent sample t-test would need two variables, one defining the groups, and the other as a measurement of interest, along with hypothesis that the mean between the groups show a difference (JMP, 2021). For example, native English speaking children and non-native English speakers. Research for a one-sample t-test involves continuous data as well as a random sample from a normal population (JMP, 2021). An example of a one-independent sample t-test is finding the mean cholesterol level for patients, using a random sample of cholesterol measurements from patients that were not taking high cholesterol medication, and comparing the mean to the known goal level.