The effect distribution: The missing piece in experimentation programs

The article argues that successful experimentation requires understanding the "effect distribution"—the true underlying impact of all product changes—rather than relying solely on individual statistically significant results. By accurately estimating this distribution, teams can avoid overestimating potential gains and use frameworks like Expected Value of Sample Information (EVSI) to better quantify the value of testing and optimize resource allocation.