Why summing your experiment wins overstates impact
Blog post from GrowthBook
The text discusses the concept of the "winner's curse" in the context of experimental results, highlighting how summing significant results from multiple experiments can lead to an overestimation of their true impact. This bias arises because experiments that pass the significance threshold often do so due to favorable random noise, causing inflated estimates. The text uses examples, such as Airbnb's findings, to illustrate how much the reported impact can differ from reality. To address this, it suggests using a "holdout" group—a subset of users not exposed to new changes—to get a more accurate measure of impact, as well as considering replication and Bayesian adjustments to correct for bias. The piece emphasizes the importance of understanding the mechanics of hypothesis testing and the limitations of aggregating results based solely on significance, warning against over-reliance on these inflated figures for decision-making.
No tracked trend matches for this post yet.
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.