His latest book is Number Sense: How to Use Big Data to Your Advantage. To better understand what A/B testing is, where it originated, and how to use it, I spoke with Kaiser Fung, who founded the applied analytics program at Columbia University and is author of Junk Charts, a blog devoted to the critical examination of data and graphics in the mass media. One of the most common methods, particularly in online settings, is A/B testing. That’s a good thing, of course, and fortunately there are lots of ways to get information without having to rely on one’s instincts. Leaders don’t want to make decisions unless they have evidence. While it’s an often-used method, there are several mistakes that managers make when doing A/B testing: reacting to early data without letting the test run its full course looking at too many metrics instead of focusing on the ones they most care about and not doing enough retesting to be sure they didn’t get false positive results. The test works by showing two sets of users (assigned at random when they visit the site) different versions of a product or site and then determining which influenced your success metric the most. It’s now used to evaluate everything from website design to online offers to headlines to product descriptions. This testing method has risen in popularity over the last couple of decades as companies have realized that the online environment is well-suited to help managers, especially marketers, answer questions like, “What is most likely to make people click? Or buy our product? Or register with our site?”. While it’s most often associated with websites and apps, the method is almost 100 years old and it’s one of the simplest forms of a randomized controlled experiment. A/B testing is a way to compare two versions of something to figure out which performs better.
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