The HR leader had not taken into account any additional factors, or conducted any comparative analysis to remove all (or as many as possible) variables other than the retention effort. The data could certainly demonstrate correlation, but it was premature to announce causation. The HR leader for that area immediately trumpeted about the latest retention effort and how it turned everything around. Lo and behold, however, suddenly in the space of just a couple of months, turnover dropped to near zero. Attempts to manage that turnover, and foster retention, were random and desperate. In my past I worked for an organization where one division had much, much higher turnover than any of the other divisions. It (like any data analysis snafu) is a slippery slope. To overly simply it, this equates to identifying correlation in data and using that assume causation.

If you read the article linked above you will see how data dredging works. Confusing “Cause” and “Correlation.” I’ve written about this before, but this is, in my experience, the most common error made, and the one most commonly committed by the media as well.There is neither space or time enough to cover every potential issue with data analysis, so what I will offer here is a brief list of common errors in order to help everyone up their game. To learn how easy it is for folks to be fooled, read this article. To compound the issue, we are all presented with more access to data, and more exposure to unsupported analysis of that data presented as truth. There’s a reason people say “garbage in, garbage out,” but how do laypeople know when data analysis is useful and when it isn’t? I have been using data to guide decisions for a long time, and I have made a ton of mistakes in analyzing that data that has led to bad results.

Dangers of data dredging