Statistical Tests for Data Analysis: Chi-Square, T-Tests, and ANOVA

To make reliable decisions in data science, business, or research, you must know if your findings are statistically significant or just random noise. This text-based course guides you through the core principles of hypothesis testing, giving you the confidence to select, run, and interpret the right statistical tests for your data. You will start with the fundamental terminology and concepts of probability, null hypotheses, and p-values before moving into practical testing scenarios. By working through clear explanations and structured written exercises, you will learn how to analyze relationships and compare group means using industry-standard statistical methodologies. You will also explore modern best practices, including how to report effect sizes and avoid common statistical pitfalls like p-hacking. What you'll learn: - Understand the foundational concepts of hypothesis testing, p-values, significance levels, and Type I/II errors - Perform and interpret Chi-Square tests to analyze relationships between categorical variables - Apply one-sample, independent, and paired T-tests to compare sample means - Configure and interpret One-Way ANOVA to analyze differences across multiple groups - Evaluate the underlying assumptions of each test, such as normality and homogeneity of variance - Practice calculating and reporting effect sizes alongside p-values to provide real-world context This course begins with essential statistical definitions and probability basics, then moves systematically through categorical analysis, two-group comparisons, and multi-group testing. It is designed for beginners, data enthusiasts, and aspiring analysts who want a solid, mathematical-light introduction to hypothesis testing without any prior advanced statistical training. Start reading today to unlock the power of statistical validation in your data workflows.