Hypothesis Testing and Biostatistics for Public Health

Public health research relies on the ability to distinguish between random chance and meaningful patterns in data. This course provides the foundational biostatistical tools needed to evaluate health interventions, analyze population trends, and interpret scientific findings accurately. You will learn how to move beyond simple averages to understand the variability inherent in all biological data. Through written explanations and practical exercises, you will gain the skills to perform standard hypothesis tests and communicate the results using modern statistical reporting standards. What you'll learn: - Understand the principles of the Central Limit Theorem and sampling distributions. - Apply t-tests and ANOVA to compare health outcomes across different groups. - Calculate and interpret p-values and confidence intervals with modern precision. - Analyze categorical data using Chi-square tests for public health surveys. - Evaluate the difference between statistical significance and practical health impact. - Practice interpreting results from real-world scientific literature. The course begins with essential terminology and the logic of probability before moving into the step-by-step process of selecting and executing statistical tests. This text-based program is designed specifically for beginners in the health sciences, requiring no prior experience with advanced mathematics or statistics. Gain the skills to interpret public health data with confidence.