Overgeneralization is a mistake in interpreting the outcomes of online controlled experiments (a.k.a. A/B tests) that can have a detrimental impact on any data-driven business. Overgeneralization is used in the typical sense of going above and beyond what the evidence at hand supports, with “evidence” being a statistically significant or non-significant outcome of an online […] Read more…
