A simple equation outperforms human decisions by at least 25%.
Does the above statistic make you still want to hire based on a gut feel? New research, analyzed and profiled in the May article of the Hardvard Business Review, explains that humans are very good at getting information on the candidates, but they are very bad at "weighing the results."
This means that hiring managers who are making their decision based on how much they "like" the candidate are actually hiring someone who won't perform as well as someone hired based off of an equation.
The Harvard Business Review titled, In Hiring, Algorithms Beat Instinct states, "If you simply crunch the applicants’ data and apply the resulting analysis to the job criteria, you’ll probably end up with a better hire."
The predictive power of numbers was researched by psychologists, Nathan R. Kuncel and Deniz S. Ones, who also found the research to hold in the hiring of any position, including the C-Suite.
Kuncel and Ones found that humans "can be thrown off course by such inconsequential bits of data as applicants’ compliments or remarks on arbitrary topics." So when you are deciding between two candidates and one of them complimented your flamingo tie, you are probably going to "like" this candidate better, therefore basing the hiring decision off of their appeal.
By leaving selection to the machines, hiring managers get a point decision, instead of compliments to base their hiring decision on. This eliminates the gut-feel hiring, unless your managers still make the decision based on candidate appeal. Unfortunately there is no system to get your hiring manager to use an algorithm model when hiring, at least not yet.
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