Tag Archives: ab testing

The A/B Testing Guide to Surviving on a Deserted Island

AB Testing Survival Guide Desert Island

The secluded and isolated deserted island setting has been used as the stage for many hypothetical explanations in economics and philosophy with the scarcity of things that can be developed as resources being a central feature. Scarcity and the need to keep risk low while aiming to improve one’s situation is what make it a […] Read More…

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Representative samples and generalizability of A/B testing results

Representative samples and generalizability of AB testing results

I see a nice trend in recent discussions on A/B testing: more and more people realize the need for proper statistical design and analysis which is a topic I hold dear as I’ve written dozens of articles and a few white-papers on. However, there are cases in which statistical validity is discussed without consideration for […] Read More…

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Analysis of 115 A/B Tests: Average Lift is 4%, Most Lack Statistical Power

Observed Percent Change Significant

What can you learn from 115 publicly available A/B tests? Usually, not much, since in most cases you would be looking at case studies with very basic data about what was tested and the outcome of the A/B test. Confidence intervals, p-values and other measurements of uncertainty will often be missing, and when present they […] Read More…

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Affordable A/B Tests: Google Optimize & AGILE A/B Testing

The problem most-often faced by owners of websites who want to take a scientific approach to improving them by using A/B testing is that they might have relatively small revenue. Thus, when the ROI calculation for the A/B test is done it might turn out that it is economically unfeasible to test. In some cases, […] Read More…

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The Google Optimize Statistical Engine and Approach

Frequentist vs Bayesian A/B testing - Google Optimize

Updated Sep 17, 2018: Minor spelling and language corrections, updates related to role of randomization and external validity / generalizability. Google Optimize is the latest attempt from Google to deliver an A/B testing product. Previously we had “Google Website Optimizer”, then we had “Content Experiments” within Google Analytics, and now we have the latest iteration: […] Read More…

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20-80% Faster A/B Tests? Is it real?

Percent Runs and Stopping Stage 1Delta

I got a question today about our AGILE A/B testing calculator and the statistics behind it and realized that I’m yet to write a dedicated post explaining the efficiency gains from using the method in more detail. This despite the fact that these speed gains are clearly communicated and verified through simulation results presented in our AGILE […] Read More…

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