One Sample t-Test: Statistical Inference with Python

Unlock the power of statistical inference by learning how to rigorously compare a sample's average to a known population value. This course will equip you with the foundational knowledge and practical skills to confidently perform, interpret, and explain the results of a one sample t-test, enabling you to make data-driven conclusions. What you'll learn: * Understand the core concepts of hypothesis testing, including null and alternative hypotheses, and common error types. * Learn the principles of the one sample t-test and its underlying assumptions for valid application. * Apply the one sample t-test using Python with written code examples to analyze data. * Interpret p-values, confidence intervals, and effect sizes to draw meaningful conclusions from your analyses. * Practice evaluating data distributions and preparing your data for statistical testing. * Communicate statistical findings clearly and effectively in written form. The course begins with a thorough explanation of statistical inference and hypothesis testing fundamentals, then guides you through the step-by-step process of conducting and interpreting the one sample t-test, including practical application using Python. This course is designed for absolute beginners in statistics and data analysis, with no prior experience in hypothesis testing or Python required. Start your journey into statistical inference and gain confidence in comparing your data today.