Foundational Statistical Hypothesis Testing

Hypothesis testing is the core skill used by data professionals to make data-driven decisions and validate assumptions. This course teaches you how to move beyond basic averages to draw statistically sound conclusions. By the end of this course, you will be able to structure statistical experiments, calculate and interpret test statistics, understand Type I and Type II errors, and confidently apply foundational tests like t-tests and chi-squared tests to common scenarios. What you'll learn: * Understand the fundamental concepts of null and alternative hypotheses and their role in scientific inquiry. * Apply the five-step process for conducting statistical hypothesis tests correctly. * Select the appropriate test (Z, T, Chi-Squared) based on data type and research question. * Practice calculating and interpreting p-values, critical values, and confidence intervals. * Analyze common errors and understand the importance of statistical power and effect size in reporting findings. The material begins with essential statistical terminology and definitions before progressing through the rigorous framework of hypothesis testing. You will work through detailed explanations and practical exercises focused on real-world data scenarios. This course is designed for absolute beginners in statistics or data science. No prior knowledge of advanced mathematics or statistical software is required. Start building your ability to validate claims with statistical evidence today.