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How to Determine Whether to Use a One-Sample, Paired or Unpaired T-Test

 
So you take statistics, and you know you need to use a t-test, but are confused about what kind of t-test to use? This simple article will show you how you can determine if a paired, unpaired, or one-sample t-test is appropriate in your particular situation.

1. Ask yourself: do I want to compare the means of two groups, or should I just don't care about how the average of a single group are compared with some numbers? If you want to compare the means of two groups, continue to step 2.

But if you only care how the average of a single group in relation to a single number, you must use a one-sample t-test. An example of a case where a one-sample t-test is appropriate would be if you are testing whether the average student spends considerably more than 2000 calories a day (e.g. Comparing the average number of calories consumed in order to see if it's significantly greater than the number of 2000).

2. If you compare means of two groups, the next ask yourself: Have the two groups of figures which we compare comes from the same people? If so, we need to use a paired samples t-test (also known as a repeated-samples t-test).

For example, let's say that we compare the weight of each person in a group of people before they went on a diet with their weight after they completed diet program. We would like to know whether each person's weight when the program is significantly greater than their weight in advance. The two sets of numbers, we compare come from the same set of people: a set represents their weight before treatment, and the second set represents their weight after treatment. This is called a within-subjects variable. In a case like this, you must use a paired samples t-test (also known as a repeated-samples t-test).

There is even a case where a paired samples t-test is appropriate. : If the researcher doing a "matched" design, where the deliberately chosen few of the items that are the same for different characteristics (e.g., age, gender, medical history, etc.) Anytime that the figures in the first and second group are paired, there is a meaningful link between a value in the first group of scores and the corresponding value in the other group of scores, a paired samples t-test is appropriate.

3. In all other cases where a t-test is appropriate, it is best to use an independent samples t-test. This is appropriate for the "between-subjects" designs, where two groups of subjects are meant to differ on a critical manipulation. For example, if testing the effect of caffeine on the growth of plants, you can have two groups: a control group, who were given water, and an experimental group of plants, which got a caffeine solution. When you use completely different plants in each group, there is no meaningful mating between the scores in the two groups, and you must use an independent-samples t-test.
 
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