Basic Statistics for Data Analysis
Learn practical statistics in simple English: questions, variables, bias, distributions, probability, confidence intervals, p-values, effect sizes, correlation, causation, and careful reporting.
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Learn judgement before tools
This course teaches the thinking learners need before using Python or R for statistics. The focus is not memorising formulas. The focus is asking better questions, reading evidence carefully, and writing conclusions that do not overclaim.
Turn vague claims into statistical questions with units, outcomes, comparisons, populations, and limits.
Use denominators, distributions, uncertainty, p-values, and effect sizes without losing practical meaning.
Separate association from causation and write evidence memos that name assumptions and limits.
What you need before starting
Basic arithmetic, percentages, simple ratios, and comfort reading small tables and simple charts.
25-35 hours across twelve modules, worksheets, checkpoints, answer keys, and a final evidence memo.
Static lessons, synthetic datasets, learner templates, and a downloadable activity pack. Videos can be added later.
Twelve-module course sequence
The order moves from questions and data quality into summaries, uncertainty, statistical tests, group comparison, causation, experiments, and final reporting.
Statistical Questions and Data Claims
- Turn a vague topic into a clear statistical question and a careful claim boundary.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Variables, Measurement, Samples, and Bias
- Identify units, variables, samples, populations, missing evidence, and likely bias.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Counts, Rates, Percentages, and Proportions
- Compare counts only after checking the denominator.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Centre, Spread, and Distribution Shape
- Describe numeric data with centre, spread, shape, and outlier caution.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Reading Charts and Distributions
- Read charts by separating what is shown from what is not proved.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Probability, Randomness, and Variation
- Build intuition for chance, repeated trials, noise, and signal.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Sampling Variation and Confidence Intervals
- Interpret estimates with uncertainty ranges instead of false precision.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Hypothesis Testing and p-values
- Interpret p-values as evidence checks, not proof.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Comparing Groups and Effect Sizes
- Compare groups using magnitude, uncertainty, and practical meaning.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Non-Parametric Thinking and Assumption Caution
- Recognize when common tests need assumption caution and simpler alternatives.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Correlation, Confounding, and Causation
- Avoid claiming causation from association alone.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Experiments, A/B Tests, and Statistical Reporting
- Bring the course together in a careful evidence report.
- Practise with a synthetic dataset, worksheet, checkpoint, and answer key.
Start the course now
The first release is text-first and worksheet-first. The activity pack includes synthetic datasets, worksheets, answer keys, and reusable learner templates.