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SWEATING THE SMALL STUFF:DOES DATA CLEANING ANDTESTING OF ASSUMPTIONS REALLYMATTER IN THE 21ST CENTURY?Topic Editor:Jason W. Osborne, University of Louisville, USAModern statistical software makes it easier than ever to do thorough data screening/cleaning andto test assumptions associated with the analyses researchers perform.However, few authors (even in top-tier journals) seem to be reporting data cleaning/screeningand testing assumptions associated with the statistical analyses being reported. Few populartextbooks seem to focus on these basics.In the 21st Century, with our complex modern analyses, is data screening and cleaning stillrelevant? Do outliers or extreme scores matter any more? Does having normally distributedvariables improve analyses? Are there new techniques for screening or cleaning data thatresearchers should be aware of?Are most analyses robust to violations of most assumptions, to the point that researchers reallydon’t need to pay attention to assumptions any more?My goal for this special issue is examine this issue with fresh eyes and 21st century methods.I believe that we can demonstrate that these things do still matter, even when using “robust”methods or non-parametric techniques, and perhaps identify when they matter MOST or inwhat way they can most substantially affect the results of an analysis.I believe we can encourage researchers to change their habits through evidence-baseddiscussions revolving around these issues. It is possible we can even convince editors ofimportant journals to include these aspects in their evaluation /review criteria, as many journalsin the social sciences have done with effect size reporting in recent years.I invite you to join me in demonstrating WHY paying attention to these mundane aspects ofquantitative analysis can be beneficial to researchers.Frontiers in Psychology August 2013 | Sweating the Small Stuff: Does data cleaning and testing of assumptions really matter in the 21st century? | 2
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