ST · 02 · Semester course
Statistical Inference
We almost never get to see the whole population — only a sample of it. This course is about what you may honestly conclude from that sample, how confident you are entitled to be, and where that confidence quietly runs out.
Content coming soon
Estimation & testing
Government Graduate College, Layyah
Coming soon
The notes for this course
The notes for this course
are on their way.
This page is deliberately empty for now. I publish each lesson only after we have covered it in class, so that what you read here matches exactly what was taught — the same notation, the same examples, the same worked problems.
Keep this link. Check back after every session, and you will always find the latest material waiting here.
- Populations and samples — what we are really trying to learn, and why a sample is all we ever get.
- Sampling distributions — the idea that makes all of inference possible.
- Point and interval estimation — a single best guess, and an honest range around it.
- Confidence intervals — including what they do not mean, which is where most people go wrong.
- Hypothesis testing — null and alternative, errors of the first and second kind, p-values read correctly.
- Applied tests — z, t and chi-square, chosen deliberately rather than by habit.