Statistical Analysis and A/B Experimentation for Business

Instructor: Alaa Hady · Language: English or Arabic · Suggested duration: 2 days · Level: Intermediate to Advanced (Level 200–300)

Objectives

• Translate ambiguous business initiatives into rigorous, testable statistical hypotheses.
• Understand probability distributions, sampling error, confidence intervals, p-values, and statistical power.
• Design, size, and analyze clean A/B tests and quasi-experiments (Difference-in-Differences) for pricing, marketing, and operational changes.
• Prevent costly false positives ("p-hacking" and early peeking) and communicate statistical vs. practical business significance.

Outline and contents

1. The scientific method in business: formulating Null (H_0) and Alternative (H_1) hypotheses
2. Descriptive statistics beyond the mean: skewness, standard deviation (\sigma), interquartile range (IQR), and outlier sensitivity
3. Sampling, Central Limit Theorem, Standard Error, and constructing 95% Confidence Intervals in DAX and Python
4. Hypothesis testing mechanics: Type I (\alpha false positive) vs. Type II (\beta false negative) errors, p-values, t-tests, and Chi-Square (\chi^2) proportion tests
5. Designing valid A/B experiments: randomization, control groups, minimum detectable effect (MDE), and sample size calculators
6. When randomized A/B testing is impossible: Matched-Market testing and Difference-in-Differences (DiD) for physical retail/operations rollouts
7. Common experimentation traps: peeking early, novelty effects, survivorship bias, Simpson's Paradox, and multiple testing corrections
8. Building an Experimentation Readout & Statistical Significance calculator directly in Power BI
9. Case Study Workshop: Evaluating a pricing promotion rollout and advising executives on go/no-go rollout

Target audience

Product analysts, marketing analysts, pricing analysts, operations engineers, and strategy professionals.

Expected outcomes

Participants can design trustworthy business experiments, calculate statistical confidence in Power BI, and stop organizations from rolling out ineffective initiatives based on random noise.