When “One Size Fits All” Isn’t Appropriate

admin Healthcare Analytics, Our Thots

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We often see improvements in our lives to make them more enjoyable, or safer. Now we see a new trend of targeted improvement.

For example, NFL football helmets have improved significantly since the Chicago (now Arizona) Cardinals last won the NFL Championship game in 1947. But they are now looking at helmet improvements by position, as clearly, a Center needs a much higher front impact helmet than a Kicker.

The same holds true for medicine. This article from FiveThirtyEight explains how Bayesian analysis provides insight into how not all drug trials should be created equal. They argue that the bar for FDA approval of clinical trials should scale to disease severity.

As you think about your data analytics career, think about what “one size fits all” assumptions you are making. Can you do a deeper dive by segmenting the population of data? And if you do, what can be gained from it? ¬†You might just stumble upon a few new ideas that could make real impact.