Elsevier

Nutrition

Volume 24, Issue 5, May 2008, Pages 492-494
Nutrition

Statistics column
Transform your data

https://doi.org/10.1016/j.nut.2008.01.004Get rights and content

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Lesson 2: Transform your data

Transforming your data means applying a non-linear function to your data—usually a log, square-root, or reciprocal function—and analyzing the results rather than the raw data. You may decide not to transform your data—if the raw data are symmetrically distributed, for example—but that should be a conscious choice. Transforming your data before analysis is like focusing a camera before taking a picture. It is almost always worthwhile and makes everything clearer. Many measurements used in

Visual clarity

An example of better visual clarity comes from Guilhardi and Church [3]. They did two experiments in which they trained rats to poke their heads into a food cup to get food. Each experiment had two phases: training (during which head pokes were rewarded) and extinction (during which head pokes stopped being rewarded).

During extinction, head pokes became less frequent. This has been observed countless times in learning experiments. To their great credit, Guilhardi and Church managed to see

Statistical clarity

The data from Guilhardi and Church also show how transformations can increase statistical clarity. Did the spread of interresponse times increase from training to extinction, as Figure 2 implies? To find out, for each rat (there were 24 rats) we can compute the standard deviation of interresponse times during 1) training and 2) extinction. Then we can compare the two sets of 24 standard deviations using a t test.

The result of that t test depends on the numbers used to compute the standard

Acknowledgments

The author thanks Saul Sternberg for helpful comments and Paulo Guilhardi for data.

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