Epidemiologists analyse clinical trials to estimate the intention-to-treat and per-protocol effects. This post applies their strategies to online experiments.
Going deeper into correct testing of different methods for bootstrap estimation of confidence intervals.
Video and summary of a talk I gave at YOW! Data on bootstrap estimation of confidence intervals.
Bootstrap sampling has been promoted as an easy way of modelling uncertainty to hackers without much statistical knowledge. But things aren’t that simple.
Causal Inference by Miguel Hernán and Jamie Robins is a must-read for anyone interested in the area.
Updating my definition of data science to match changes in the field. It is now broader than before, but its ultimate goal is still to support decisions.
There’s a lot of misleading content on the estimation of customer lifetime value. Here’s what I learned about doing it well.
Seven common mistakes to avoid when working with data, such as ignoring uncertainty and confusing observed and unobserved quantities.
It seems like anyone who touches data can call themselves a data scientist, which makes the title useless. The work they do can still be useful, though.
A web tool I built to interpret A/B test results in a Bayesian way, including prior specification, visualisations, and decision rules.