# Teaching and review guide

This is an unpiloted foundation-course design. Read the data dictionary and methods caveats before teaching. The synthetic mechanism is deliberately visible; use it to contrast what the author knows with what an analyst could infer from an observational export.

Begin with decisions and denominators, not formulas. Ask learners to explain a result in ordinary language before introducing notation. Let them calculate the six-value summary by hand. Explain disagreements caused by quartile conventions rather than declaring a different documented convention wrong.

After Lesson 4, check grain, missingness and sampling-versus-assignment. After Lesson 8, check SD-versus-SE, interval interpretation and p-values. After Lesson 11, ask learners to defend a recommendation using effect size and limitations without relying on “significant.” Use the three checkpoint marking guides for specific feedback.

The programming route is optional. A learner who can reason correctly using supplied statistical outputs can meet the core outcomes. A learner whose reference script runs but who cannot explain its denominator or assumptions has not met them. Public checks evaluate only selected arithmetic and labels in the learner's own result file; the reference tests validate the supplied package.

Common misconceptions to watch for: treating every CSV row as independent; interpreting missing as zero; deleting outliers by rule alone; treating a sample mean as a guarantee for an individual; assuming sample size removes bias; reversing conditional probabilities; using p as the probability a hypothesis is true; declaring equivalence after non-rejection; confusing percent with percentage points; calling correlation causal; describing synthetic findings as real evidence.

For the capstone, accept different well-supported recommendations. Require visible uncertainty, a clear population and traceable values. Do not award a passing mark while a critical integrity failure remains. Ask a second reviewer to score a few early submissions and compare disagreements before using the proposed 80/100 threshold widely.

Before release, run the acceptance checklist in VALIDATION.md on target browsers, operating systems and assistive technologies. Pilot with beginners, record where they pause, whether terms need an earlier introduction, actual completion time, exercise errors and perceived workload. Revise the sequence and rubric from that evidence. The present estimates must not be advertised as measured completion times.