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T- Test

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T-tests are handy hypothesis tests in statistics when you want to compare means. You can compare a sample mean to a hypothesized or target value using a one-sample t-test. You can compare the means of two groups with a two-sample t-test. If you have two groups with paired observations (e.g.before and after measurements), use the paired t-test.



How do t-tests work? How do t-values fit in? In this series of posts, I’ll answer these questions by focusing on concepts and graphs rather than equations and numbers. After all, a key reason to use statistical software like Minitab is so you don’t get bogged down in the calculations and can instead focus on understanding your results.

calculate probabilities and assess hypotheses.

What are T-Tests?
Sometimes, we don’t just look at or describe one group of data. Instead, we want to look at two groups of data and compare them. We want to see if the two groups are different. T-tests are often used to compare the means from two different groups of data. They can help you find out if means are significantly different from one another or if they are relatively the same. If the means are significantly different, you can say that the variable being manipulated, your Independent Variable (IV), had an effect on the variable being measured, your Dependent Variable (DV). You will probably be asked to do two popular types of t-tests in SPSS so we will talk about each.

Independent Samples T-Tests
Background | Enter Data | Analyze Data | Interpret Data | Report Data

These types of t-tests are used to compare groups of participants that are not related in any way. The groups are independent of one another. So, participants in one group have no relationship to participants in the second group. This is sometimes called a between-subjects design.

Paired Samples T-Tests
Background | Enter Data | Analyze Data | Interpret Data | Report Data


These types of tests are used to compare groups that are related in some way. There are so many ways that participants in two groups can be related. One way is that participants in the first group are the same as participants in the second group. This is sometimes called a repeated measures design. A second way is that participants in the first group are genetically related to participants in the second group. For example, a pair of twins could be divided up so one twin participated with the first group and the other twin participated with the second group. A third way is if participants in one group are matched with participants in a second group by some attribute. For example, if a participant in the first group rates high on depression, researchers might try to find a participant in the second group that also rates high on depression.


















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