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Statistics Foundation

Learn statistics from ideas to inference and regression.

A zero-coding foundation course for students who want clear statistical reasoning before software. All modules and lessons are now available for full study.

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Course access

The full Statistics Foundation course is open now. All modules and lessons are available for full study.

Learning design

Built for understanding, not memorisation.

Conversational lectures with Mr. R, Amelia, Ben, Chloe and Daniel
Detailed theoretical notes with equations and derivations
Interactive labs for visual intuition
Worked examples with careful interpretation
Practice studios and quizzes
No R, Python or coding required

By the end

Students should be able to reason statistically.

Explain what statistics is used for
Summarise and compare data correctly
Understand probability and uncertainty
Interpret confidence intervals and p-values
Understand regression and model interpretation
Prepare for biostatistics, health data science and applied research methods

Module pages

All module pages and lesson pages are open for full study.

Students can explore the full course structure now. Each module page shows the complete lesson sequence, formulas and learning pathway for the fully open course.

01Module open

Statistical Thinking

5 lessons

Populations, samples, variables, data types, tables, graphs and the purpose of statistical reasoning.

Statistical questionsData typesSamplingTables and graphs

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02Module open

Descriptive Statistics

5 lessons

Centre, spread, quartiles, percentiles, skewness, outliers and comparing groups descriptively.

Mean and medianSpreadQuartilesGroup comparison

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03Module open

Probability Foundations

5 lessons

Probability rules, sample spaces, conditional probability, independence, dependence and Bayes’ theorem.

Probability rulesConditional probabilityIndependenceBayes

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04Module open

Statistical Inference

6 lessons

Sampling distributions, standard error, confidence intervals, hypothesis testing, p-values, power and study design.

Standard errorConfidence intervalsHypothesis testsPower

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05Module open

Regression Foundations

5 lessons

Correlation, simple regression, least squares, residuals, multiple regression, confounding and logistic regression.

CorrelationLinear regressionConfoundingLogistic regression

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Lesson access

Open now. All modules and lessons are available for full study.

All lesson pages are now available for full study.

1.1"Open"

Module 1

What is statistics?

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1.2"Open"

Module 1

Types of data

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Module 1

Populations, samples and variables

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1.4"Open"

Module 1

Tables and graphs

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Module 1

Sampling methods

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2.1"Open"

Module 2

Organising data

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Module 2

Measures of centre

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Module 2

Measures of spread

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Module 2

Quartiles and percentiles

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Module 2

Comparing groups descriptively

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3.1"Open"

Module 3

What is probability?

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Module 3

Events, sample spaces and probability rules

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Module 3

Conditional probability

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Module 3

Independence and dependence

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Module 3

Bayes’ theorem and diagnostic reasoning

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Module 4

Sampling distributions and standard error

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Module 4

Confidence intervals

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Module 4

Hypothesis testing framework

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Module 4

P-values, errors and power

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Module 4

Sample size and study design

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Module 4

Choosing the right inference method

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5.1"Open"

Module 5

Correlation and simple relationships

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Module 5

Simple linear regression

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Module 5

Least squares and residuals

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Module 5

Multiple regression and confounding

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Module 5

Logistic regression foundations

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Try the ideas visually

Visual intuition before formulas.

These demos support the full course. They help students see distribution shape and confidence interval behaviour before moving into formal notation.

Interactive demo

Normal Distribution Explorer

Move the mean and standard deviation. The mean shifts the centre. The standard deviation controls the spread.

Interpretation: increasing the standard deviation spreads probability over a wider range. Changing the mean moves the centre without changing the total area under the curve.

Interactive demo

Confidence Interval Simulator

Change the sample size and confidence level. Wider intervals are more likely to capture the true value, but they are less precise.

Interpretation: 9/10 displayed intervals contain the true mean. Increasing the sample size narrows intervals. Increasing the confidence level widens intervals.

Recommended start

Begin with the open foundation lesson.

Lesson 1.1 introduces the purpose of statistics, statistical questions, populations, samples, variables and why uncertainty matters.

Start Module 1