Dina Bavli·10h agoDigital Experiments: Can We Trust This Result?The strongest-looking result in the dashboard can still come from an experiment that quietly stopped being the one you designed
Dina Bavli·4d agoDigital Experiments: How Much Evidence Is Enough?Power, sample size and duration only make sense after you decide what evidence would actually change the decision
Dina Bavli·Oct 20Digital Experiments: You’re Not Testing What You ThinkTreatment design is the contract between what your hypothesis says, what production delivers, and what your analysis eventually claims
Dina Bavli·Oct 16Digital Experiments: Your Control Group Isn’t a Control GroupAssignment mechanisms, interference, and SUTVA — when randomizing by user quietly corrupts what you measured
Dina Bavli·Oct 13Digital Experiments: Not Every Experiment Is an A/B TestWhat to do when the intervention already shipped, randomization is impossible, or the system itself refuses to stay inside treatment and…
Dina Bavli·Oct 9Digital Experiments: Your Metric Is a Business DecisionWhat happens when the metric improves and the product outcome gets worse?
Dina Bavli·Oct 6Digital Experiments: The Outcome You Never Get to SeePotential outcomes, counterfactuals, and why causal inference begins with a value that is missing by construction.
Dina Bavli·Oct 1Digital Experiments: More Data Won’t Fix Your BiasWhy a bigger observational sample just gives you a more precise estimate of the wrong number
Dina Bavli·Sep 24Digital Experiments: Should We Even Run an Experiment?Before sample size, significance, or even treatment and control, there is a more expensive question: is this experiment worth running at…A response icon1A response icon1
Dina Bavli·Aug 13When Agents Talk, But the Protocol Changed: Migrating a2a-sdk 0.3 to 1.xA Practical Before/After Guide — With a Real Side-by-Side Diff in the Workshop Repo
