Run Inference & Hypothesis Tests

Overview

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.

Syllabus

Module 1: Confidence-Interval Estimation - Foundation

Apply confidence-interval estimation to compare conversion rates across segments and present the statistical significance.

Module 2: Type I/II Error Trade-offs - Core Application

Evaluate Type I/II error trade-offs for a proposed test and recommend appropriate alpha and beta thresholds.

Module 3: Two-Sample t-Tests & Power Analysis - Integration & Assessment

Conduct a two-sample t-test in Python/R, interpret p-values, translate outcomes into plain-language business recommendations, and analyze test power under varying sample sizes.