![]() ![]() 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.
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