Tag: statistics
-
A/B testing, zero-inflated (truncated) distributions and power
Naive A/B testing just uses t-tests or proportion tests, with the assumption that at large sample sizes, the right statistical test does not matter that much. I explore the case of a zero-inflated upper-bounded Poisson distribution and find that using the wrong test can require 3x the sample size to achieve the same statistical power, a difference large enough to matter in a real business setting.
-
Gaussian Processes: a versatile data science method that packs infinite dimensions
Last semester, I learned about Gaussian Processes. They seemed really intriguing at the first glance, and it turned out they are even more intriguing as you dig deeper. This post is an application-oriented intro to Gaussian Processes. I’ll cover GP regressions, forecasting for time series and usage of GPs in bayesian optimization among other things.
-
Interpretation of log transformations in linear models: just how accurate is it?
Log-transformations and their interpretation as percentage impact is taught in every introductory regression class. But are most people aware that there is a hidden approximation behind the percentage-based intuition? One that may not be appropriate in some cases?