Why “Statistical Significance” Is Pointless
Here’s a better framework for data-driven decision-making. Data scientists are in the business of decision-making. Our work is focused on how to make informed choices under uncertainty. And yet, when it comes to quantifying that uncertainty, we often lean on the idea of “statistical significance” — a tool that, at best, provides a shallow understanding. In this article, we’ll explore why “statistical significance” is flawed: arbitrary thresholds, a false sense of certainty, and a failure to address real-world trade-offs.
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Here’s a better framework for data-driven decision-making.
Data scientists are in the business of decision-making. Our work is focused on how to make informed choices under uncertainty.
And yet, when it comes to quantifying that uncertainty, we often lean on the idea of “statistical significance” — a tool that, at best, provides a shallow understanding.
In this article, we’ll explore why “statistical significance” is flawed: arbitrary thresholds, a false sense of certainty, and a failure to address real-world trade-offs.
