[100% Off] Statistics &Amp; A/B Testing For Data Science: Practice Exams

Validate your analytics skills with 200 practice scenarios on Hypothesis Testing, Regression, and A/B Testing.

Added on June 4, 2026 100% Off Coupon 2 min read

What you’ll learn

• Evaluate Hypothesis Tests (Null vs. Alternative) and interpret p-values to determine the statistical significance of business metrics.
• Design and analyze A/B Tests
• accurately calculating required sample sizes while avoiding Type I (False Positive) and Type II errors.
• Apply Descriptive Statistics (Variance
• Standard Deviation
• Z-Scores) to identify extreme outliers in raw datasets.
• Understand core Probability Distributions (Normal
• Binomial
• Poisson) and evaluate the assumptions required for Linear and Logistic Regression.

Requirements

• A foundational understanding of high-school level algebra and basic mathematics. No prior coding experience in Python or R is required
• as this course focuses entirely on mathematical theory and applied business logic.

Description

Anyone can import a machine learning library in Python, but without a deep understanding of statistics, you are simply guessing. Welcome to the Statistics & A/B Testing practice assessments! In the era of big data, companies do not just want charts; they want mathematical proof that a new feature, a financial investment, or a marketing campaign is actually working. This comprehensive practice test course provides you with 200 expertly crafted, highly unique practice questions designed to simulate the rigorous technical probability interviews given at top tech companies.

Across these four rigorous practice exams, you will be thrown into high-stakes analytical scenarios. You will test your ability to evaluate the historical risk and variance of mutual fund portfolios, run A/B tests to optimize conversion rates on job recruitment portals, and analyze customer churn metrics using logistic regression. The questions push you to evaluate complex mathematical trade-offs: When is a p-value of 0.05 actually misleading? Why must you always calculate a minimum sample size before starting an A/B test? How does the Central Limit Theorem (CLT) allow you to analyze non-normal data?

Every single question in this course is unique and includes a detailed explanation of the “why” behind the correct statistical logic. By reviewing these explanations, you will learn to spot the mathematical biases that ruin predictive models. If you are preparing for a technical data science interview, building complex machine learning pipelines, or simply want to stop relying on “gut instinct” to make business decisions, this is your ultimate testing ground. Enroll today and trust the math!

Course locale: English (US)

Course instructional level: Intermediate Level

Course category: Teaching & Academics

Course subcategory: Math