# 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. - Source: https://productovi.com/courses/statistics-for-pm-and-marketers - Free, no sign-up needed to read. ## Modules ### Module 1. Describing data Typical values, spread, outliers and charts: how to summarise any product metric honestly. - [Mean, median and mode: the typical value](https://productovi.com/courses/statistics-for-pm-and-marketers/typical-value.md): Mean, 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. - [Measures of spread: range, SD and quartiles](https://productovi.com/courses/statistics-for-pm-and-marketers/spread.md): Two 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. - [Outliers and skewed data: how to find them](https://productovi.com/courses/statistics-for-pm-and-marketers/outliers-and-skew.md): Why 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. - [Types of charts: choosing the right one](https://productovi.com/courses/statistics-for-pm-and-marketers/charts.md): The 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. ### Module 2. Comparing two groups From samples to significance: when a difference between two groups is real and when it's noise. - [Sample vs population and the normal distribution](https://productovi.com/courses/statistics-for-pm-and-marketers/samples-and-normal-distribution.md): Sample 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. - [Two-sample t-test](https://productovi.com/courses/statistics-for-pm-and-marketers/t-test.md): The two-sample t-test (Student's t-test): comparing the means of two groups and deciding whether the gap is bigger than chance. - [Mann–Whitney: when data isn't normal](https://productovi.com/courses/statistics-for-pm-and-marketers/mann-whitney.md): A rank-based test for skewed metrics like revenue and session length. - [p-value and statistical significance](https://productovi.com/courses/statistics-for-pm-and-marketers/p-value.md): What 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. ### Module 3. A/B testing in practice Confidence intervals, power, conversion tests and the pitfalls that break real experiments. - [95% confidence intervals](https://productovi.com/courses/statistics-for-pm-and-marketers/confidence-intervals.md): Building a 95% confidence interval from its formula and margin of error, so you report a range of plausible values instead of one number. - [Power analysis and sample size](https://productovi.com/courses/statistics-for-pm-and-marketers/power-and-sample-size.md): A power analysis tells you how many users an experiment needs to detect the effect you care about. - [Conversion rates and the two-proportion z-test](https://productovi.com/courses/statistics-for-pm-and-marketers/conversion-rate-tests.md): Comparing conversion rates between variants with a two-proportion z-test and reading uplift correctly. - [Pitfalls: peeking, multiple testing, novelty effect](https://productovi.com/courses/statistics-for-pm-and-marketers/ab-testing-pitfalls.md): The most common ways real A/B tests go wrong, and how to avoid them. ### 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. - [One-way ANOVA: comparing many groups](https://productovi.com/courses/statistics-for-pm-and-marketers/anova.md): Testing three or more variants at once with a one-way ANOVA, without inflating false positives. - [Two-way ANOVA and interactions](https://productovi.com/courses/statistics-for-pm-and-marketers/multifactor-anova.md): A two-way ANOVA for when two factors act together, e.g. a promo that works on mobile but not on desktop. - [Paired t-test: before and after](https://productovi.com/courses/statistics-for-pm-and-marketers/paired-tests.md): Comparing the same users before and after a change with a paired t-test or the Wilcoxon signed-rank test. - [Repeated measures ANOVA](https://productovi.com/courses/statistics-for-pm-and-marketers/repeated-measures.md): Tracking the same users across several points in time with a repeated measures ANOVA or the Friedman test. ### Module 5. Relationships Correlation, causation and regression: how metrics move together and how to predict one from others. - [Correlation coefficient: how two metrics move together](https://productovi.com/courses/statistics-for-pm-and-marketers/correlation.md): Measuring how two metrics move together with the correlation coefficient (Pearson and Spearman), and why that alone proves nothing about cause. - [Simple linear regression](https://productovi.com/courses/statistics-for-pm-and-marketers/linear-regression.md): Simple linear regression: predicting one metric from another with a line of best fit, and checking the fit with R squared. - [Multiple linear regression](https://productovi.com/courses/statistics-for-pm-and-marketers/multiple-regression.md): Multiple linear regression: separating the effect of several drivers at once, and the two classic traps, overfitting and multicollinearity. - [Logistic regression and classification](https://productovi.com/courses/statistics-for-pm-and-marketers/logistic-regression.md): Logistic 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. ### Module 6. Modelling and segmentation Forecasts, clustering users into segments, finding survey drivers, and choosing the right test. - [Models and forecasting](https://productovi.com/courses/statistics-for-pm-and-marketers/models-and-forecasts.md): What a model is, how a simple forecasting model extends a trend, and how to tell whether the forecast is any good. - [Clustering for customer segmentation](https://productovi.com/courses/statistics-for-pm-and-marketers/clustering.md): Customer segmentation from the data: letting cluster analysis suggest the segments instead of guessing them. - [Factor analysis: surveys and NPS drivers](https://productovi.com/courses/statistics-for-pm-and-marketers/factor-analysis.md): Finding the few hidden themes behind many survey questions. - [Course recap: choosing the right test](https://productovi.com/courses/statistics-for-pm-and-marketers/choosing-the-right-test.md): A decision guide that ties the whole course together.