Editing Is it chance? Use a T-Test to identify how likely an intervention worked
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− | + | You tried an intervention and want to see if it worked. How likely is it that the results were chance? | |
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− | + | Solution | |
− | + | One of the simplest tests is a “T-Test”, sometimes called a “Student T Test”. | |
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− | + | Statisticians use the concept of _P Value_ to discuss the how often a result might appear to be significant even when it’s not. While this crude measure doesn’t describe all the ways something might happen due to chance, generally the lower the P Value, the better. Professional scientists, especially those who understand statistics, will get touchy if you claim a result based purely on P Values, but for Personal Science purposes, it’s a good start. There is no “correct” cutoff value that can determine the likelihood that something is due to chance alone, but traditionally people assume that anything under 0.05 deserves a closer look. | |
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− | + | Here’s an example for how to do this in Excel. | |
− | + | Suppose you’d like to know if taking a melotonin supplement will help you sleep longer. You’ve measured your daily sleep, taking the supplements on some days (the “intervention”) and not on others (“control”). | |
− | + | A simple spreadsheet might look like this: | |
− | + | ![](image-20200828173214548.png) | |
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Track your sleep under two columns: one for nights when you took the supplement, and the other for nights you didn’t. | Track your sleep under two columns: one for nights when you took the supplement, and the other for nights you didn’t. | ||
− | The built-in Excel statistical function | + | The built-in Excel statistical function `T.TEST` will calculate the P-Value when you give it two ranges, the “intervention” (nights we took melotonin) and the “control” (nights without). |
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− | + | See the screenshot for the exact formula in this case: | |
− | + | `=T.TEST(array1,array2,tails,type)` | |
− | + | Enter a `1` for `tails` (because we’re only interested in one measurement, sleep) and a `2` for type (because in this case our samples are not of the same length). | |
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− | + | The P Value in this example, `0.24`, is above `0.05` and therefore we will assume that any difference in sleep between the nights is due to pure chance. |