- 1What is statistical inference?A large trial found a 30% lower risk of cancer among older women taking vitamin D3 and calcium, and the authors still reported the result as inconclusive. Working through that disagreement introduces statistical conclusion validity and the two major approaches to inference.7 min
- 2Imagine a world where the treatment does nothingFrequentist inference judges the one study you ran by comparing it against studies that were never run. Building that imaginary world one figure at a time — null hypothesis, simulation, and the Central Limit Theorem — with a trial of lay-counselor depression treatment in India as the example.9 min
- 3Retaining or rejecting the null hypothesisOnce the null distribution exists, the decision about your result is close to mechanical. Alpha as a long-run error rate chosen before the study is run, the p-value as a conditional probability, and false positives and false negatives shown across simulated studies.6 min
- 4What a p-value doesn’t tell youThe p-value answers a narrower question than most readers think it does. Statistical against clinical significance, why published p-values pile up just under the threshold, and what effect sizes and confidence intervals offer in place of a yes-or-no verdict.8 min
- 5The Bayesian approachA positive rapid COVID test means something different early in a pandemic than it does during a surge, and the difference is the base rate you started from. Bayesian inference built from that intuition, then the same trial analyzed both ways — nearly identical numbers, different interpretations.8 min