Is data cleaning and the testing of assumptions relevant in the 21st century?

You must understand fully what your assumptions say and what they imply. You must not claim that the “usual assumptions ” are acceptable due to the robustness of your technique unless you really understand the implications and limits of this assertion in the context of your application. And you must absolutely never use any statistical method without realizing that you are implic-itly making assumptions, and that the validity of your results can never be greater than that of the most questionable of these (Vardeman and Morris, 2003, p. 26). Modern quantitative studies use sophisticated statistical anal-yses that rely upon numerous important assumptions to ensure the validity of the results and protection from mis-estimation of outcomes. Yet casual inspection of respected journals in various fields shows a marked absence of discussion of the mundane, basic staples of quantitative methodology such as data cleaning or testing of assumptions, leaving us in the troubling position of being surrounded by intriguing quantitative findings but not able to assess the quality or reliability of the knowledge base of our field. Few of us become scientists in order to do harm to the liter-ature. Indeed most of us seek to help people, improve the world in some way, to make a difference. However, all the effort in the world will not accomplish these goals in the absence of valid, reliable, generalizable results—which can only be had with clean (non-faulty) data and assumptions of analyses met. WHERE DOES THIS IDEA OF DATA CLEANING AND TESTING