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The independent-samples t-test compares the measurement of a variable to a constant, a reference value or a preset theoretical value. The t-test is used when you compare the average of two independent samples, unlike the ANOVA, where two or more sample average is compared(Urdan, 2011).

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It allows you to test the null hypothesis that the data in a sample maintains, the same behavior as those of the home population. When we are making our comparison, we need to know if there is any statistical significance in the two independent sample average(Urdan, 2011). When conducting a t-test, you need to have the two independent samples that you are comparing (example: men and women, children and adults, Hispanics and Caucasians) and one dependent variable, which the value or scores may vary(Urdan, 2011).

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An example is when you want to compare the IQ of 100 men to 100 women, you will conduct an independent sample t-test. Men and women are the two independent samples because men do not belong in the same group with the women(Urdan, 2011), and the dependent variable is the IQ. We would have a set of scores for the IQ of men and a set of scores for the IQ of women. We want to know if there is a statistical significance in men and women’s IQ. Our null hypothesis and our alternative hypothesis would be formed, and we would conduct a t-test to conclude if our results are due to chance.

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