Statistics with Data Storytelling
Aabha AI Academy · Foundation course · Review edition 2026-10-14
Purpose and prerequisites
Learn to ask a statistical question, check the evidence, quantify a meaningful difference and communicate a recommendation with honest uncertainty. The course is for beginners who can use rows and columns, divide numbers, interpret a percentage and save a file. No calculus, SQL, Python or prior statistics is required for the core reading-and-calculator route.
A useful readiness check: calculate 15/60 as a percentage; explain why one customer can have several orders; and distinguish an unknown rating from a rating of zero. Answers: 25%; an order-grain table can contain repeated customer IDs; unknown supplies no rating value. If these are unfamiliar, begin slowly with Lesson 1 and ask a tutor to work the first examples with you.
This course follows the existing Aabha Python and SQL foundations without repeating their syntax lessons. Python's CSV, NumPy/pandas and plotting modules support the optional companion. SQL's grain, aggregates, joins and validation modules support extraction. Here, the new material is sampling design, uncertainty, inferential assumptions, effect interpretation and decision-focused communication. You do not need to complete both earlier courses to begin.
Sequence and learning evidence
| Lesson | Focus | Evidence you will produce | Provisional effort |
|---|---|---|---|
| 1 | Question, population, grain, metric and audit | An analysis contract and denominator check | 1–1.5 h |
| 2 | Mean, median, spread and outliers | Hand-worked summaries and an outlier policy | 1.5–2 h |
| 3 | Distribution and comparison charts | A chart choice and a repaired misleading chart | 1–1.5 h |
| 4 | Sampling, assignment, bias, missingness, dependence | Selection and missingness explanation | 1.5–2 h |
| 5 | Conditional probability and base rates | A checked count table and probability interpretation | 1–1.5 h |
| 6 | Sampling variability and standard errors | An SD-versus-SE explanation | 1–1.5 h |
| 7 | Confidence intervals and their limits | A mean interval and correct interpretation | 1.5–2 h |
| 8 | Independent comparisons and Welch tests | Difference, interval, p-value and assumptions | 1.5–2 h |
| 9 | Practical effects and fair comparisons | Threshold decision and weighted comparison | 1–1.5 h |
| 10 | Correlation and descriptive regression | Slope, residual and model criticism | 1.5–2 h |
| 11 | Audience, evidence and recommendations | A short management brief and chart description | 1–1.5 h |
| 12 | Reproducible capstone workflow | A staged decision brief and methods appendix | 1 h orientation |
Allow another 4–6 hours for the capstone and 1–2 hours for setup and checkpoint review. A reasonable initial planning range is about 22–30 hours, with additional time for unfamiliar tools. These are unpiloted estimates, not observed completion times or promises.
Study method
Read a lesson, predict a worked result, check its arithmetic, then explain it in ordinary words. Attempt its three workbook exercises before opening the separate answers. Take Checkpoint A after Lesson 4, B after Lesson 8 and C after Lesson 11. Each checkpoint has six questions and a separate explained marking guide. Revisit missed concepts rather than memorizing the answer key.
The three checkpoints total 60 points. A suggested readiness target is at least 16/20 on each, with all misconceptions about grain, causation, confidence intervals and p-values corrected before the capstone. This is an instructional recommendation, not a validated placement test. The capstone uses its own 100-point rubric, with a target of at least 80 and no critical integrity failure.
What the files do
Open index.html for the complete offline learner guide. practice.html contains the workbook, checkpoint questions and capstone brief. solutions.html contains the separate explained answers and a reference capstone. Markdown sources are editable. The HTML pages are reading guides; they do not run Python, record progress or automatically grade learners.
Three CSVs contain the fictional data: a raw shipment export, its clean counterpart and an independent simulated checklist trial. The generator is available so the known mechanism can be inspected. Optional Python scripts reproduce the reference analysis and charts. Keep raw and learner outputs separate. Never substitute reference test success for a learner submission.
Scope boundaries
This course teaches introductory descriptive analysis and carefully bounded inference for a simple simulated independent-groups study. It does not train learners to conduct high-stakes research, diagnose medical conditions, make financial decisions, estimate production causal effects, select a valid model automatically, perform clustered/time-series inference, or deploy a predictive system. Regression is descriptive and introductory. Survey weighting, power analysis, equivalence testing, Bayesian inference and multivariable causal adjustment are identified as next topics rather than taught as complete methods.
All people, operations, data and decisions are fictional. No website was published, no learning management system was modified and no accreditation or certificate is implied.