Hypothesis Testing Essentials with Excel and Python

In a data-driven world, distinguishing between random noise and meaningful trends is a critical skill for any professional. Understanding how to mathematically validate an assumption allows you to move beyond intuition and make decisions backed by statistical evidence. This course guides you from basic statistical concepts to performing rigorous hypothesis tests for means and proportions. You will learn how to approach data with a critical eye, using two of the most popular tools in the industry to reach reliable conclusions. What you'll learn: - Understand the foundational logic of the Central Limit Theorem and statistical inference. - Perform hypothesis tests for population means and proportions within Excel. - Apply modern Python libraries, such as SciPy and Pandas, to execute statistical calculations. - Interpret p-values, significance levels, and confidence intervals in a business context. - Identify and mitigate Type I and Type II errors in data analysis. - Evaluate effect size to determine the practical impact of statistical findings. The material begins with essential terminology and probability concepts before moving into step-by-step written explanations for executing tests in both Excel and Python. You will progress through reading practical examples and reviewing code snippets that demonstrate how to automate your analysis. This course is designed for beginners looking to build a solid foundation in data analysis. No prior experience with statistics or programming is required to get started. Start building your data-driven decision-making skills today.