Exploring data distribution prior to analysis: Benefits and pitfalls

The choice of appropriate statistics is important for all research and should be based on the study design, the research question and whether assumptions about the data are upheld or not. Authors commonly select so-called ‘parametric’ tests, which are associated with several assumptions about the data, e. g. that data are normally distributed in the population of interest. Thankfully, many authors who submit manuscripts to IJSM check that their data meets the necessary assumptions for use of parametric statistical analyses. Nevertheless, some authors do not complete this important step, and are, therefore, asked by editors to justify their choice of statistics. Those authors who do check underlying statistical assumptions sometimes do so using an inappropriate aspect of the data and/or believe that the only solution to violation of parametric assumptions is the adoption of non-parametric ‘ranked’ tests. The aim of this statistical note is to illustrate the importance of checking assumptions with the correct aspect of the data and arriving at the correct choice of analysis, which may involve a mathematical transformation of the data prior to analysis.