TRANSFORMING BIOLOGICAL DATA TO GAUSSIAN FORM

  • 1 January 1980
    • journal article
    • research article
    • Vol. 44  (1) , 43-51
Abstract
Much of the statistical analysis of biological data depends on the assumption that the data are Gaussian (or normal). Well-known procedures which use this assumption are t-tests, analysis of variance, regression estimation and their attendant tests. If the data are not Gaussian, nonparametric statistical techniques can be used if they exist, but they often require larger amounts of data to obtain equally precise results. If the data are not Gaussian an approach to their analyses lies in trying to find a transformation which will render them Gaussian. Data thus transformed to a Gaussian form can be analyzed validly, using standard statistical techniques. The process of finding a good transformation of the data has often been an arbitrary and ad hoc one. The purpose of this study is to examine a particular technique for attempting to render non-Gaussian data Gaussian, and to illustrate its applicability and breadth of use.

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