{"id":116067,"date":"2023-05-22T08:00:56","date_gmt":"2023-05-22T08:00:56","guid":{"rendered":"https:\/\/staging.wingify.com\/blog\/formulate-ab-test-hypothesis\/"},"modified":"2026-09-15T14:03:57","modified_gmt":"2026-09-15T08:33:57","slug":"formulate-ab-test-hypothesis","status":"publish","type":"post","link":"https:\/\/wingify.com\/blog\/formulate-ab-test-hypothesis\/","title":{"rendered":"A\/B Test Hypothesis Definition, Tips and Best Practices"},"content":{"rendered":"\n
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Incomplete, irrelevant or poorly formulated A\/B test hypotheses are at the root of many neutral or negative tests.<\/p>\n

Often we imagine that doing A\/B tests to improve your e-commerce site\u2019s performance means quickly changing the color of the \u201cadd to cart\u201d button will lead to a drastic increase in your conversion rate, for example. However, A\/B testing is not always so simple.<\/p>\n

Unfortunately, implementing random changes to your pages won\u2019t always significantly improve your results \u2013 there should be a reason behind your web experiments<\/a>.<\/p>\n

This brings us to the following question: how do you know which elements to experiment with and how can you create an effective AB test hypothesis?<\/p>\n

Determine the problem and the hypothesis<\/h2>\n

Far too few people question the true origins of the success (or failure) of the changes they put in place to improve their conversion rate.<\/p>\n

However, it\u2019s important to know how to determine both the problem and the hypothesis<\/strong> that will allow you to obtain the best results.<\/p>\n

Instead of searching for a quick \u201cDIY\u201d solution, it\u2019s often more valuable in the long term to take a step back and do two things:<\/p>\n<\/div>\n\n\n\n

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  1. Identify the real problem<\/strong> \u2013 What is the source of your poor performance? Is it a high bounce rate<\/a> on your order confirmation page, too many single-page sessions,\u00a0 a low-performing checkout CTA or something more complex?<\/li>\n\n\n\n
  2. Establish a hypothesis<\/strong> \u2013 This could show the root of the problem. For example, a great hypothesis for A\/B testing could be: \u201cOur customers do not immediately understand the characteristics of our products when they read the pages on our e-commerce site. Making the information more visible will increase the clicks on the \u201cadd-to-cart\u201d button.\u201d<\/li>\n<\/ol>\n\n\n\n

    The second step may seem very difficult because it requires a capacity for introspection and a critical look at the existing site. Nevertheless, it\u2019s crucial for anyone who wants to see their KPIs improve drastically<\/a>.<\/p>\n\n\n\n

    If you\u2019re feeling a bit uncomfortable with this type of uncertainty around creating an effective hypothesis, know that you\u2019ve come to the right place.<\/p>\n\n\n\n

    What is an A\/B test hypothesis?<\/h2>\n\n\n\n

    Technically speaking, the word hypothesis has a very simple definition:<\/p>\n\n\n\n

    \u201cA proposal that seeks to provide a plausible explanation of a set of facts and which must be controlled against experience or verified in its consequences.\u201d<\/p>\n\n\n\n

    The first interesting point to notice in this definition is \u201cthe set of facts to be explained.\u201d In A\/B testing, a hypothesis must always start with a clearly identified problem<\/strong>.<\/p>\n\n\n\n

    A\/B tests should not be done randomly, or you risk wasting time.<\/p>\n\n\n\n

    Let\u2019s talk about how to identify the problem:<\/p>\n\n\n\n