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How To... > Work with Statistical Objects > Test Hypotheses > Compare Means from Raw Data (Two-Sample t-Test)
| 1. | Create a new test by dragging one from the or by choosing . |
| 2. | From the pop-up menu in the test’s upper-right corner, choose . You have two groups and something you’ve measured. You want to know if the difference of means between the two groups of the thing you’ve measured is significant. We’ll use an example to illustrate this. Suppose you are doing an experiment with plants and fertilizer. Some of the plants get the fertilizer and some don’t. |
There are two ways to use this test, depending on how the data are structured.
The preferred way of structuring the data is shown here. Each case is a plant, and the attribute Group tells whether or not the plant got fertilizer. To assign attributes to the estimate object, the Group attribute, being categorical, goes to the second line of the attribute pane, and the Height attribute goes to the first line. (Notice that the test is in terse mode.)
The less preferred way of structuring the data is to use one attribute for the values of one group and a second attribute for the values of the other group. Notice that this means that a single case in the collection doesn’t really make any sense; it represents a pair of plants, but there isn’t any good reason for a particular pair to be assigned to the same case. Drop either attribute on either line in the test object; the difference will be the first attribute minus the second.
By default, Fathom calculates the significance of the difference in means using unpooled variances. Click on the phrase “unpooled variances” for a pop-up menu that allows you to switch between unpooled variances and pooled variance. Use pooled variance when you have reason to believe that the standard deviation of the values is the same for both groups. Unpooled variances use weaker assumptions and produce somewhat larger p-values than pooled.
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