Data Science and Machine Learning

Currently Playing: Ep 38 — Machine Learning A/B Testing - Moving One Shelf on Purpose (A/B Testing)

#ai #artificialintelligence #machinelearning #ml #datascience #statistics #algorithms Core Idea: Controlled experiments establish causation, not just correlation. Concept: A/B testing (controlled randomized experimentation). Why it matters: The gold standard for proving cause, not just noticing coincidence (a perfect callback to the correlation vs. causation of Ep 21). Core mechanic: Splitting subjects into control and treatment groups, setting sample sizes, and computing statistical significance (e.g., using scipy.stats.ttest_ind). Example: Testing two different layouts of a webpage to see which one results in higher user conversion. Common mistake: Ending a test too early before reaching true statistical significance. The Rule: "Not what happened alongside it. What changed because of it." Next Clue: The same shelf, changing slowly, night after night (bridging to Ep 39: Time Series Basics).