The Student's t-test is one of the simplest statistical tests available. It comes in three types: independent samples t-tests, matched pairs t-tests, and one-sample t-tests.
A t-test is used to determine if two sets of data are different enough to conclude that the underlying populations from which the data were drawn are also different. In a controlled experiment, a t-test could be used to determine if control and experimental conditions differed after the application of some treatment, such as evaluating the effectiveness of some learning intervention. In survey research, a t-test could be used to determine if responses drawn from different populations differ, such as evaluating whether domestic and international students view the Georgia Tech OMS program equally favorably.
The following sources explain simple t-tests:
Simple t-tests will take care of many analyses, but there are slightly more complicated versions for more complex analyses. For example, t-tests assume that the two groups don't interact. But what if you wanted to test the effectiveness of a learning tool without doing a controlled experiment? What if you simply wanted to evaluate whether the students knew more after using the tool than they knew before? For that, you would use a matched t-test, where you pair up connected values.
The following sources explain matched t-tests:
A third kind of t-test, the one-sample t-test, can be used when we know the population mean and want to evaluate whether or not a particular sample matches that population mean. For example, we may want to evaluate whether incoming Georgia Tech OMS students have average GRE scores that match the GRE average: the GRE average is known, and we may take one sample of incoming OMS students and compare their mean GRE scores to the GRE average.
The following sources explain one-sample t-tests:
Generally, though, you won't do the math for t-tests by hand. You might use advanced statistical like SPSS or R, but you can also take advantage of simple online calculators like the ones below:
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