Statistical inference

Planned

In learning order. These are not pages yet.

1. Sampling distributions: a statistic is a random variable tooIntro
2. Bias and variance of an estimatorIntro
3. What a 95% confidence interval actually meansIntro
4. The delta method: standard errors for a function of an estimateAdvanced undergrad
5. Why the sample variance divides by n − 1Intro
6. Maximum likelihood: sliding the parameter to fit the dataAdvanced undergrad
7. Hypothesis tests: the null, the alternative, and the rejection regionIntro
8. Fisher information and identifiability: when data can pin down a parameterGraduate
9. The t distribution: what changes when σ is unknownAdvanced undergrad
10. What a p-value measures, and the usual ways it gets misreadIntro
11. Statistical power and sample sizeIntro
12. Likelihood ratio tests and Wilks' theoremAdvanced undergrad
13. Sufficient statistics: compressing data without losing informationGraduate
14. Method of moments and GMM: matching sample moments to the modelAdvanced undergrad
15. Chi-square goodness of fit and contingency tablesAdvanced undergrad
16. Multiple testing: why 20 tests produce a false positiveAdvanced undergrad
17. When the model is wrong: misspecified MLE and sandwich standard errorsGraduate