Free online courseBasic Statistics Full Course: Descriptive Stats, Hypothesis Testing, ANOVA, Regression and Power

Course Description

Statistics can feel like a set of disconnected formulas until you see how each idea supports better decisions. This free online course guides you from the first building blocks of descriptive statistics to the reasoning behind modern inference, so you can confidently interpret data instead of memorizing steps. You will learn how to summarize data with meaningful measures of center and spread, recognize skewness and outliers, and understand why tools like standard deviation, variance, and the normal distribution matter in everyday analysis.

From there, the course focuses on the logic of uncertainty: sampling, the central limit theorem, standard error, and confidence intervals. Rather than treating these topics as abstract theory, you develop an intuition for what estimates mean, how precision changes with sample size, and how to communicate results in a clear, defensible way. You also move into hypothesis testing and p-values with an emphasis on interpreting outcomes correctly, including how confidence intervals and tests connect, what degrees of freedom represent, and what it really means to reject or fail to reject a null hypothesis.

As your skills grow, you will practice choosing appropriate methods for real scenarios, including comparisons between groups and before-and-after designs. You will understand when to use different t-tests, how one-way and repeated-measures ANOVA answer questions across multiple groups, and how categorical data methods like chi-square, z-tests for proportions, and exact tests fit into sound analysis. Nonparametric alternatives are introduced to help you handle situations where common assumptions are not a good match.

The course also develops your ability to model relationships in data. You will learn to interpret correlation, decide between Pearson and Spearman approaches, and build an understanding of linear regression from the idea of best fit through inference, residual reasoning, and model interpretation. By the end, concepts like Type I and Type II errors, statistical power, and sample size become practical tools for planning studies and evaluating results. With practice questions throughout, you finish with a structured, decision-oriented statistics foundation you can apply in school, projects, and entry-level data work.