Statistical Inference and Hypothesis Testing

Statistics isn't just about numbers—it's about making confident decisions that drive business success. This Short Course was created to help data analysts accomplish statistical inference with precision and clarity. By completing this course, you'll be able to apply confidence intervals to compare conversion rates across segments, evaluate error trade-offs in experimental design, conduct hypothesis tests in Python/R, and visualize power analysis to guide decision-making. By the end of this course, you will be able to: Apply statistical rigor to business problems with measurable impact Transform raw data into statistically-backed insights Communicate findings that stakeholders trust and act upon This course is unique because it bridges statistical theory with real-world application, using authentic business scenarios from leading tech companies. To be successful in this project, you should have a background in basic statistics and programming experience in Python or R

• Evaluate effect size alongside p-values to assess true business impact
• Balance Type I and Type II errors in experiment design based on business risk
• Translate statistical results into clear, actionable business recommendations

• Basic familiarity with concepts and terminology
• Readiness for applied exercises or case-based work