Microsoft runs thousands of A/B test experiments per month. And, bets millions of dollars on the outcomes of these experiments.Not surprisingly, the company needs to be fully confident they can trust their A/B testing results.Listen to this informative 17-minute talk to learn about:
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Calculating the Minimum Detectable Effect (MDE) can feel like a hair-pulling, speculative exercise. Luckily, this step-by-step guide is here to show you exactly how to accurately calculate your MDE to yield powerful, trustworthy test results.
Low sample testing is problematic and can result in hugely mis-leading data where tests appear to be enormous winners but, in actuality, aren't. This article presents a practical 5-step approach to overcome the traps of low sample testing and arrive at valid, trustworthy results. What are the 5-steps? Find out here.
Understanding and calculating statistical significance is quite complex. Many experimenters don’t truly know what statistical significance is or how to derive a statistically significant test result. To properly call a winning (or losing) A/B test, it’s really important to clearly understand what a statistically significant result is and means. This article, written in plain English is here to set it all straight for you.