Hypothesis Testing in Practice

Hypothesis Testing in Practice is a focused Data Science course that teaches you how to evaluate evidence, compare groups, and turn statistical results into sound recommendations. You will learn to make defensible statistical decisions from real data with confidence, clarity, and practical judgment.

Apply Hypothesis Testing In Practice To Real Data Science Decisions

• Build a clear foundation for choosing, running, and interpreting common hypothesis tests.
• Learn how p-values, confidence intervals, effect sizes, and power work together in real analysis.
• Practice applied workflows for A/B tests, categorical data, regression coefficients, and group comparisons.
• Communicate statistical evidence clearly while avoiding p-hacking, overclaiming, and misleading conclusions.

This course teaches practical hypothesis testing for Data Science, from research questions to defensible recommendations.

You will begin with the purpose of hypothesis testing, learning how to translate a research question into testable hypotheses and reason about sampling variation, null models, evidence, significance levels, p-values, and decision rules. From there, the course moves into interpretation, showing how confidence intervals and effect sizes help you distinguish statistical significance from practical importance. You will then work through core tests for means, proportions, paired comparisons, categorical data, correlation, and regression, while learning how assumptions, diagnostics, nonparametric alternatives, sample size, power, and minimum detectable effects shape responsible analysis. Applied lessons on A/B testing, multiple comparisons, false discoveries, p-hacking, optional stopping, and clear reporting help connect statistical methods to real Data Science work. By the end of Hypothesis Testing in Practice, you will be able to make defensible statistical decisions from real data with confidence, clarity, and practical judgment, and explain your conclusions in a way that supports better decisions.