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Statistical Methods in Online A/B Testing: Statistics for data-driven business decisions and risk management in e-commerce
Statistical Methods in Online A/B Testing is a comprehensive guide to statistics in online controlled experiments.
Statistical Methods in Online A/B Testing: Statistics for data-driven business decisions and risk management in e-commerce
Item #: 20513184

Statistical Methods in Online A/B Testing: Statistics for data-driven business decisions and risk management in e-commerce

Item #: 20513184

ZAR 1685

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Statistical Methods in Online A/B Testing is a comprehensive guide to statistics in online controlled experiments.
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What Stands Out

Practical Insights
Delivers actionable statistical methods tailored for A/B testing in e-commerce, empowering businesses to make informed decisions and enhance risk management strategies effectively.
Comprehensive Coverage
Covers essential topics in statistical methodology, ensuring readers understand various techniques and their applications for optimizing online business performance and strategic outcomes.
User-Friendly Approach
Designed for practitioners, this book simplifies complex statistical concepts, making it accessible for business professionals who seek to leverage data-driven approaches without deep statistical expertise.

Product Details

Discover statistical methods for data-driven decisions & risk management in e-commerce. Find the best products on Ubuy South Africa. Shop now for a wide range of options.
  • Comprehensive guide to statistics in online controlled experiments (A/B tests)
  • Accessible to readers with little to no prior experience in statistics
  • Practical examples from website testing illustrate each concept
  • Focused on helping readers make data-driven business decisions
  • Suitable for conversion rate optimizers, product managers, and more
  • Addresses common mistakes and misguided practices in online A/B testing
Item Weight2 lbs (910 grams)

Who Should Buy?

Suitable For
  • E-commerce Analysts

    Analysts can leverage this book to improve data interpretation and drive strategic decisions through robust statistical methods.

  • Marketing Professionals

    Marketers can use insights from A/B testing statistics to optimize campaigns, enhancing conversion rates and ROI effectively.

  • Data Scientists

    Data scientists will find practical applications of statistical theory for experimental design, aiding in advanced data analysis.

Not Suitable For
  • Beginners in Statistics

    Newcomers may struggle with the concepts without prior foundational knowledge in statistics and data analysis methods.

  • Casual Readers

    Individuals looking for light, engaging reading may find the technical nature of statistical methods unappealing.

  • Highly Technical Experts

    Seasoned statisticians may not find new insights, as the material could be too basic or reiterative for them.

Product Description

Statistical Methods in Online A/B Testing: Statistics for data-driven business decisions and risk management in e-commerce

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Statistics Editorial Review

"Statistical Methods in Online A/B Testing" is a comprehensive guide to online A/B testing. The book covers both simple explanations and underlying equations where necessary. The author, Georgi, provides his commentary and opinions on optimal approaches. The book covers esoteric but relevant topics such as one-sided hypothesis tests, confidence intervals, holdouts, and percentage changes, and thoroughly clarifies commonly misunderstood concepts. However, some readers feel that certain critical concepts are not explained and justified thoroughly. The author also introduces multiple concepts all at once, causing confusion. The book would have benefited from a section on case studies after each chapter. Overall, the book is highly recommended for anyone serious about experimentation and A/B testing.

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Pros

  • Thorough coverage of esoteric but relevant topics
  • Offers commentary and opinions on optimal approaches
  • Provides clear explanations and deeper appreciation

Cons

  • Certain critical concepts are not explained and justified thoroughly

Product Price History

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