Day 4/60: Crossing the Chasm — Inferential Statistics & Hypothesis Testing
Yesterday, we focused on descriptive statistics — how to summarize the data you already have. But in enterprise data science, the data you…Continue reading on Medium »
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Yesterday, we focused on descriptive statistics — how to summarize the data you already have. But in enterprise data science, the data you…Continue reading on Medium »
Complete guide to running one-sample and two-sample Z-tests in R using BSDA::z.test(), the manual pnorm/qnorm method, and how to choose between a Z-test and a t-test for your data....
So, those exciting results I presented at StanCon about Walnutpie? Let’s pull back the curtain. Confirmation bias in testing Those stellar results were the result of a bug in the w...
Your software will happily run the test. It will not tell you the result is meaningless.Continue reading on Medium »
Frequentist confidence intervals and Bayesian credible intervals answer different questions, and confusing them can distort product decisions The post The 95% Illusion: Why Your Co...
Почему одинаковые данные могут давать разные p-value? На простом примере с монетой разбираю, как правило остановки меняет частотный вывод, а затем связываю это с подглядыванием в A...
Partha Sarathi Samal of Paramount on replaying 18 months of incidents against 4,200 tests, the proven, duplicate and unproven verdicts, and their limits.
Not every 95% confidence interval actually delivers 95% coverage. A 3,000-month simulation study shows why the Wilson score interval provides more reliable uncertainty estimates th...
A clean sample can sound stronger than it is. Someone checks a handful of items, finds no exceptions, and the sentence wants to become broader than the evidence: the process worked...
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