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.
Most important, we’ll learn how to move beyond the binary mindset of significant vs. non-significant, and adopt a decision-making framework grounded in economic impact and risk management.
Imagine we just ran an A/B test to evaluate a new feature designed to boost the time users spend on our website — and, as a result, their spending.
The control group consisted of 5,000 users, and the treatment group included another 5,000 users. This gives us two arrays, named treatment and control, each of them containing 5,000 values representing the spending of individual users in their respective groups.
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