{"id":54793,"date":"2020-10-13T14:56:47","date_gmt":"2020-10-13T09:26:47","guid":{"rendered":"https:\/\/vwo.com\/blog\/?p=54793"},"modified":"2026-02-27T17:52:35","modified_gmt":"2026-02-27T12:22:35","slug":"ab-testing-is-like-chess","status":"publish","type":"post","link":"https:\/\/vwo.com\/blog\/ab-testing-is-like-chess\/","title":{"rendered":"A\/B Testing Is Like Chess"},"content":{"rendered":"\n

Hi ? I am Paras Chopra<\/a>, founder & chairman of VWO<\/a>. Hope you are finding my fortnightly posts outlining a new idea or a story <\/a>on experimentation and growth<\/em> useful. Here is the 4th letter.<\/p>\n\n\n\n

The rules of chess are easy to remember: a pawn moves one step forward, the queen can go anywhere and the end goal of the game is to protect the king. Once you remember the rules, the game is easy to set up and fun to play.<\/p>\n\n\n\n

But being easy in principle doesn\u2019t mean it\u2019s also easy in practice. Truly mastering chess can take decades of daily practice and requires memorizing thousands of nuances about opening moves, closing moves, and opponent strategies.<\/p>\n\n\n\n

Download Free: A\/B Testing Guide<\/span><\/em><\/a><\/h2>\n\n\n\n

A\/B testing<\/a> is very similar to chess in that<\/em> sense.<\/p>\n\n\n\n

\"A<\/figure>\n\n\n\n

In principle, A\/B testing<\/a> is simple: you have two variations, each of which gets equal traffic. You measure how they perform on various metrics. The one that performs better gets adopted permanently.<\/p>\n\n\n\n

In practice, however, each word in the previous paragraph deserves a book-length treatment. Consider unpacking questions like:<\/p>\n\n\n

1\/ What is \u201ctraffic\u201d in an experiment?<\/strong><\/h2>\n\n\n

Is it visitors, users, pageviews, or something else? If it is visitors, what kinds of visitors? Should you include all visitors on the page being tested, or should you only include the visitors for whom the changes being tested are most relevant?<\/p>\n\n\n

2\/ What is \u201cmeasurement\u201d in an experiment?<\/strong><\/h2>\n\n\n

If a user landed on your page and did not convert, when do you mark it as non-conversion? What if the user converts after<\/em> you\u2019ve marked it as non-conversion? How do you accommodate refunds? If different user groups have markedly different conversion behavior, does it even make sense to group them during measurement? If you group them, how do you deal with Simpson\u2019s paradox<\/a>?<\/p>\n\n\n

3\/ What types of \u201cvarious metrics\u201d should you measure?<\/strong>\u00a0<\/h2>\n\n\n

Should you have one metric to measure the performance of variations, or should you have multiple? If you measure revenue, should you measure average revenue per visitor, average revenue per conversion, 90th percentile revenue, frequency of revenue, or all of them? Should you remove outliers from your data or not?<\/p>\n\n\n\n

Download Free: A\/B Testing Guide<\/span><\/em><\/a><\/h2>\n\n\n

4\/ What does \u201cperform better\u201d mean?<\/strong><\/h2>\n\n\n

Is 95% statistical significance good enough? What if it is 94%? What if the new variation is not performing significantly better but feels it should? Do you take a bet on those? What if one metric improved but another that should have improved as well actually became worse? How real is Tyman\u2019s law<\/a>, which states that extreme improvements are usually due to instrumentation effort?<\/p>\n\n\n\n

For the skeptic, these questions may seem like a needless pedantic exercise. But, without rigor, why bother doing A\/B testing in the first place?<\/p>\n\n\n\n

Nobody likes their ideas, and efforts go to waste, so we latch onto any glimmer of success we see in our A\/B tests. It\u2019s relatively easy to get successful A\/B tests because it presents many avenues for misinterpretation to a motivated seeker. It\u2019s only human to be biased.<\/p>\n\n\n\n

But because of this lack of rigor in A\/B testing, many organizations that get spectacular results from their A\/B tests fail to see an impact on their business<\/strong>. Contrast this with organizations who take their experimentation seriously: Booking.com<\/a>, AirBnB, Microsoft, Netflix, and many other companies with a culture of experimentation<\/a> know that getting good at A\/B testing takes deliberate commitment.<\/p>\n\n\n\n

So, next time someone tells you that A\/B testing doesn\u2019t work, remind yourself that it\u2019s like saying chess is a boring game just because you\u2019re not good at it.<\/p>\n\n\n\n

If you enjoyed reading my letter, do send me a note with your thoughts at paras@vwo.com. I read and reply to all emails ?<\/p>\n","protected":false},"excerpt":{"rendered":"

The rules of chess are easy to remember: a pawn moves one step forward, the queen can go anywhere and the end goal of the game<\/p>\n","protected":false},"author":14,"featured_media":55809,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"post_read_time":3,"footnotes":""},"categories":[10185],"tags":[],"feature":[1852,10526],"industry-type":[],"product":[],"role":[],"region":[],"class_list":["post-54793","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-letters-from-the-founder","feature-ab-testing","feature-experimentation-platform"],"acf":[],"yoast_head":"\nA\/B Testing is Like Chess | Letters From The Founder | VWO<\/title>\n<meta name=\"description\" content=\"In this post, VWO's founder & chairman, Paras Chopra, shares his analogy on how A\/B testing & chess are very similar in some sense.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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