Basic Statistics for Data Analysis
Learn the practical statistics needed to read data, compare groups, understand uncertainty, avoid overclaiming, and write careful evidence-based conclusions.
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Progress overview
Course sequence
- Module 1: Statistical Questions and Data ClaimsTurn a vague topic into a clear statistical question and a careful claim boundary.
- Module 2: Variables, Measurement, Samples, and BiasIdentify units, variables, samples, populations, missing evidence, and likely bias.
- Module 3: Counts, Rates, Percentages, and ProportionsCompare counts only after checking the denominator.
- Module 4: Centre, Spread, and Distribution ShapeDescribe numeric data with centre, spread, shape, and outlier caution.
- Module 5: Reading Charts and DistributionsRead charts by separating what is shown from what is not proved.
- Module 6: Probability, Randomness, and VariationBuild intuition for chance, repeated trials, noise, and signal.
- Module 7: Sampling Variation and Confidence IntervalsInterpret estimates with uncertainty ranges instead of false precision.
- Module 8: Hypothesis Testing and p-valuesInterpret p-values as evidence checks, not proof.
- Module 9: Comparing Groups and Effect SizesCompare groups using magnitude, uncertainty, and practical meaning.
- Module 10: Non-Parametric Thinking and Assumption CautionRecognize when common tests need assumption caution and simpler alternatives.
- Module 11: Correlation, Confounding, and CausationAvoid claiming causation from association alone.
- Module 12: Experiments, A/B Tests, and Statistical ReportingBring the course together in a careful evidence report.
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Use only the supplied fictional and synthetic data. Do not paste real private data into course exercises.