I have written several blog posts on how Bayes’ Rule can help you make better business decisions and application of this theorem. One of the areas where Bayes’ Rule is most often neglected is in hiring decisions. Often, rational and data driven individuals and organizations abandon the rules of optimal decision-making and rely on intuition.
At its core, Bayes’ Rule shows how you can optimize the chance of a correct decision by looking at previous data points that encompass the decision you are trying to make. In the case of hiring, this analysis would be more effective by looking at the metrics and data that shows who succeeds, looking at what makes someone successful in the position you are hiring for and reducing the impact of data that does not lead to good hiring decisions.
What most companies end up doing is using data as a filter but then hiring based on intuition. If you really want to make good decisions, you need to understand your intuition is only one (weak) data point and base the decision on Bayes’ Theorem, using past data to make the optimal decision.
What has worked for others
First, look at the position you are hiring for and identify the most successful people (at other companies or at your’s) in the field and “reverse engineer” their background. What experience(s) did they have before they were hired? What is their educational background (school, degree, extra curricular activities, etc.)? Using Bayes’ Rule, if you are hiring for a Director of Social Media and find that 90 percent of the top performing Directors of Social Media went to Texas A&M, then the chances of making a good hire from Texas Tech is already at less than 10 percent. Continue reading “Bayes’ Theorem Part 6: Making the best hiring choices”