Georgi Georgiev

Creator of 250 calculators and author of their supporting information.
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Main areas of expertise

• mathematical and statistical modelling
• statistical design and analysis of experiments
• UX design and development of software calculators
• applied measurement theory
• online A/B testing

Education and professional background

Georgi holds a degree in Network Technologies. He is the author of the highly influential book "Statistical Methods in Online A/B Testing" (2019), numerous articles and white papers on statistics and the design of experiments. He has been a lecturer at multiple live and online events, delivering talks on topics such as statistics, online controlled experiments, and data analytics.

As a software creator, Georgi has been developing advanced statistical calculators for experimentation and data science professionals since 2012. Major effects of his work include the popularization of Group-sequential tests, non-inferiority testing, and development of software tools to apply these to A/B testing practice. He was distinguished as the winner of the 2024 Experimentation Thought Leadership Awards in the Data & Analytics category for his contributions to the field.

Georgi is a reviewer at the "Computational Statistics and Data Analysis" journal by Elsevier and at KDD Conference, contributing reviews since 2023 (see his ORCID profile )

Georgi has been developing a wide variety of calculators for GIGAcalculator since 2018. His passion for finding mathematical models that explain or predict the world around us has motivated him to explore topics as diverse as finance, health & fitness, physics, transportation, construction, and to even delve into the science of punching harder.

Publications

Georgi's works have been cited thousands of times in scientific papers, books, media publications, industry blogs, and more. Some of his more notable works include:

• Statistical Methods in Online A/B Testing (2019) ISBN: 9781694079725
• The Business Value of A/B Testing (2024)
• The frequentist vs Bayesian split in online experimentation before and after the ‘abandon statistical significance’ call (2024)
• Statistical Power, MDE, and Designing Statistical Tests (2022)
• How to Punch Harder – Increase Your Punching Power Using Physics (2020)
• Stop Using the Occam’s Razor Principle (2020)
• The Effect of Using Cardinality Estimates Like HyperLogLog in Statistical Analyses (2020)
• What is a P-value in Statistics (2020)
• Has Interest in Data Science Peaked Already? (2020)
• Compound Annual Growth Rate (CAGR) – A Complete Guide (2020)
• What is Six Sigma Process Control and Why Most Get It Wrong (1.5 Sigma Shift) (2019)
• “All Models Are Wrong” Does Not Mean What You Think It Means (2019)
• R squared Does Not Measure Predictive Capacity or Statistical Adequacy (2019)
• Directional Claims Require Directional (Statistical) Hypotheses (2018)
• The Hidden Costs of Bad Statistics in Clinical Research (2018)
• Confidence Intervals & P-values for Percent Change / Relative Difference (2018)
• Beyond “One Size Fits All” A/B Tests (2017)
• Statistical Significance in A/B Testing – a Complete Guide (2017)
• One-tailed vs Two-tailed Tests of Significance in A/B Testing (2017)
• Statistical Design in Online A/B Testing (2017)
• Why Every Internet Marketer Should Be a Statistician (2014)