Free online courseStatistics

Course Description

Strengthen your math skills and learn to think clearly with data in this free online statistics course designed for school-level learning and lasting real-world usefulness. Statistics is more than formulas: it is a practical way to describe what is happening, measure uncertainty, and make decisions when outcomes are not guaranteed. By the end, you will be able to interpret common summaries, evaluate evidence, and communicate conclusions in a way that is both logical and easy to justify.

You will start by building a solid descriptive foundation, learning how measures like the mean, variance, and standard deviation capture different aspects of a data set. Instead of memorizing steps, you will practice recognizing what each measure is telling you, why it changes from one data set to another, and how sample-based calculations connect to the idea of a population. That intuition becomes essential as you move from describing data to modeling randomness.

From there, you will develop an understanding of random variables and probability models, including discrete and continuous cases, and why probability density behaves differently from probabilities you can count. You will work with major distributions such as binomial and Poisson, and connect them to expected value so you can summarize long-run outcomes for repeated experiments and everyday scenarios.

The course then builds toward the big ideas that power inference. You will see how the law of large numbers and the normal distribution help explain patterns that appear across many types of data, and how tools like z-scores support comparison and interpretation. You will also understand why the central limit theorem is so important, especially when working with sample means, standard error, and what changes when sample size grows.

Next, you will learn how to estimate and test claims with confidence intervals, margins of error, and hypothesis testing, including p-values and the difference between one-tailed and two-tailed tests. You will compare z and t approaches, recognize common error types, and practice drawing conclusions responsibly rather than overclaiming. Finally, you will connect statistics to relationships between variables through regression, correlation, coefficient of determination, and methods for analyzing categorical or grouped data such as chi-square tests and ANOVA. Throughout, practice exercises help turn concepts into dependable skills you can use in school, exams, projects, and data-informed conversations.