Soon, third-party cookies will be blocked by most major web browsers leaving optimizers and experimenters desperately picking up the crumbled pieces.
We'll have no easy way to track user events or behaviors -- let alone measure conversions.
What's an optimizer to do?
In this informative webinar replay, renowned data analytics consultant, Joseph A. Hayon, will tell you!
He shares the latest on:
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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.