Statistical Inference
4concepts
32flashcards
29minutes of reading
- 01 Confidence Intervals and Coverage What the 95% in a 95% interval refers to, why the Wald interval for a proportion is badly behaved near zero, and how a confidence interval differs from the credible interval people usually think they are reading.
- 02 Estimators, Bias, Variance and Consistency What makes one estimator better than another, why the mean squared error splits cleanly into bias squared plus variance, and why an unbiased estimator is frequently the wrong thing to want.
- 03 P-Values, Multiplicity and the Garden of Forking Paths What a p-value actually claims, why running twenty tests guarantees a false discovery, and the difference between controlling the family-wise error rate and controlling the false discovery rate.
- 04 The Bootstrap and Resampling Resampling the data you have to simulate the sampling distribution you never observed, why the percentile interval is not always the right one, and the specific statistics for which the bootstrap silently fails.