Trainer
Product managers
Marketers

Statistics for Product Managers and Marketers

A free statistics course for product managers and marketers: read metrics honestly, run A/B tests you can trust and explain the numbers to your team, on realistic product data.

Free24 lessons~16 h 27 min of learning

A cartoon cat sits next to a laptop with bar charts and an open box of cat food.

Start the course

Module 1: Describing data
Typical values, spread, outliers and charts: how to summarise any product metric honestly.
1. 1.1Mean, median and mode: the typical valueMean, median, mode and trimmed mean on real-looking CatChow orders: what each one answers, why one bulk order can fool the average, and how not to average averages.~48 min
  1.1 Mean, median and mode: the typical value
  ~48 min · 11 exercises
  Start Read theory
2. 1.2Measures of spread: range, SD and quartilesTwo couriers with the same average delivery time, and why only one keeps the promise: measures of spread from range and variance to standard deviation (calculated step by step), quartiles, IQR and the coefficient of variation.~48 min
  1.2 Measures of spread: range, SD and quartiles
  ~48 min · 11 exercises
  Start Read theory
3. 1.3Outliers and skewed data: how to find themWhy product metrics have long tails, how to find outliers with the IQR rule, and what to do with them: fix errors, segment different customers, and never delete whales without thinking.~44 min
  1.3 Outliers and skewed data: how to find them
  ~44 min · 11 exercises
  Start Read theory
4. 1.4Types of charts: choosing the right oneThe main types of charts (histograms, box plots, bar, pie, scatter and line charts): which question each one answers, how bin width and axes change the story, and the classic ways charts mislead.~47 min
  1.4 Types of charts: choosing the right one
  ~47 min · 11 exercises
  Start Read theory
Module examComplete 4 more lessons to unlock
Module 2: Comparing two groups
From samples to significance: when a difference between two groups is real and when it's noise.
1. 2.1Sample vs population and the normal distributionSample vs population: why we study samples to learn about all users, why the bell-shaped normal distribution shows up everywhere, and how the central limit theorem ties them together.~39 min
  2.1 Sample vs population and the normal distribution
  ~39 min · 11 exercises
  Start Read theory
2. 2.2Two-sample t-testThe two-sample t-test (Student's t-test): comparing the means of two groups and deciding whether the gap is bigger than chance.~39 min
  2.2 Two-sample t-test
  ~39 min · 11 exercises
  Start Read theory
3. 2.3Mann–Whitney: when data isn't normalA rank-based test for skewed metrics like revenue and session length.~41 min
  2.3 Mann–Whitney: when data isn't normal
  ~41 min · 11 exercises
  Start Read theory
4. 2.4p-value and statistical significanceWhat a p-value really says, what it doesn't, how the 0.05 significance level turns it into a decision, and statistical versus practical significance.~39 min
  2.4 p-value and statistical significance
  ~39 min · 11 exercises
  Start Read theory
Module examComplete 4 more lessons to unlock
Module 3: A/B testing in practice
Confidence intervals, power, conversion tests and the pitfalls that break real experiments.
1. 3.195% confidence intervalsBuilding a 95% confidence interval from its formula and margin of error, so you report a range of plausible values instead of one number.~40 min
  3.1 95% confidence intervals
  ~40 min · 11 exercises
  Start Read theory
2. 3.2Power analysis and sample sizeA power analysis tells you how many users an experiment needs to detect the effect you care about.~38 min
  3.2 Power analysis and sample size
  ~38 min · 11 exercises
  Start Read theory
3. 3.3Conversion rates and the two-proportion z-testComparing conversion rates between variants with a two-proportion z-test and reading uplift correctly.~44 min
  3.3 Conversion rates and the two-proportion z-test
  ~44 min · 11 exercises
  Start Read theory
4. 3.4Pitfalls: peeking, multiple testing, novelty effectThe most common ways real A/B tests go wrong, and how to avoid them.~37 min
  3.4 Pitfalls: peeking, multiple testing, novelty effect
  ~37 min · 11 exercises
  Start Read theory
Module examComplete 4 more lessons to unlock
Module 4: Many groups and before/after
ANOVA, interactions, paired tests and repeated measures for comparing more than two groups or the same users over time.
1. 4.1One-way ANOVA: comparing many groupsTesting three or more variants at once with a one-way ANOVA, without inflating false positives.~40 min
  4.1 One-way ANOVA: comparing many groups
  ~40 min · 11 exercises
  Start Read theory
2. 4.2Two-way ANOVA and interactionsA two-way ANOVA for when two factors act together, e.g. a promo that works on mobile but not on desktop.~39 min
  4.2 Two-way ANOVA and interactions
  ~39 min · 11 exercises
  Start Read theory
3. 4.3Paired t-test: before and afterComparing the same users before and after a change with a paired t-test or the Wilcoxon signed-rank test.~40 min
  4.3 Paired t-test: before and after
  ~40 min · 11 exercises
  Start Read theory
4. 4.4Repeated measures ANOVATracking the same users across several points in time with a repeated measures ANOVA or the Friedman test.~41 min
  4.4 Repeated measures ANOVA
  ~41 min · 11 exercises
  Start Read theory
Module examComplete 4 more lessons to unlock
Module 5: Relationships
Correlation, causation and regression: how metrics move together and how to predict one from others.
1. 5.1Correlation coefficient: how two metrics move togetherMeasuring how two metrics move together with the correlation coefficient (Pearson and Spearman), and why that alone proves nothing about cause.~45 min
  5.1 Correlation coefficient: how two metrics move together
  ~45 min · 11 exercises
  Start Read theory
2. 5.2Simple linear regressionSimple linear regression: predicting one metric from another with a line of best fit, and checking the fit with R squared.~42 min
  5.2 Simple linear regression
  ~42 min · 11 exercises
  Start Read theory
3. 5.3Multiple linear regressionMultiple linear regression: separating the effect of several drivers at once, and the two classic traps, overfitting and multicollinearity.~39 min
  5.3 Multiple linear regression
  ~39 min · 11 exercises
  Start Read theory
4. 5.4Logistic regression and classificationLogistic regression for yes/no outcomes such as churn or conversion: odds, the odds ratio, a decision threshold, and how to measure how good the predictions are.~40 min
  5.4 Logistic regression and classification
  ~40 min · 11 exercises
  Start Read theory
Module examComplete 4 more lessons to unlock
Module 6: Modelling and segmentation
Forecasts, clustering users into segments, finding survey drivers, and choosing the right test.
1. 6.1Models and forecastingWhat a model is, how a simple forecasting model extends a trend, and how to tell whether the forecast is any good.~38 min
  6.1 Models and forecasting
  ~38 min · 11 exercises
  Start Read theory
2. 6.2Clustering for customer segmentationCustomer segmentation from the data: letting cluster analysis suggest the segments instead of guessing them.~39 min
  6.2 Clustering for customer segmentation
  ~39 min · 11 exercises
  Start Read theory
3. 6.3Factor analysis: surveys and NPS driversFinding the few hidden themes behind many survey questions.~40 min
  6.3 Factor analysis: surveys and NPS drivers
  ~40 min · 11 exercises
  Start Read theory
4. 6.4Course recap: choosing the right testA decision guide that ties the whole course together.~40 min
  6.4 Course recap: choosing the right test
  ~40 min · 12 exercises
  Start Read theory
Module examComplete 4 more lessons to unlock

Statistics for Product Managers and Marketers

Module 1: Describing data

Module 2: Comparing two groups

Module 3: A/B testing in practice

Module 4: Many groups and before/after

Module 5: Relationships

Module 6: Modelling and segmentation