{"id":18730,"date":"2022-01-13T09:17:22","date_gmt":"2022-01-13T09:17:22","guid":{"rendered":"https:\/\/www.convertize.com\/?p=18730"},"modified":"2026-04-27T02:13:32","modified_gmt":"2026-04-27T01:13:32","slug":"ab-testing","status":"publish","type":"post","link":"https:\/\/www.convertize.com\/ab-testing\/","title":{"rendered":"A Simple Guide to A\/B Testing in 2026"},"content":{"rendered":"Reading Time:<\/span> 8<\/span> minutes<\/span><\/span>

What is A\/B Testing? Definition:<\/h2>\r\n

A\/B Testing<\/strong> lets you compare the conversion rates of two versions of a page by showing half your visitors the current version\u00a0of your site (the ‘Control’) and the other half the modified page (the ‘Treatment’). By comparing which version got more signups, sales or any other metric, you can determine which version is more effective.<\/p>\r\n

By running A\/B tests you can verify\u00a0hypothesis with statistically significant data<\/a> from an experiment done with real visitors, rather than making\u00a0uninformed decisions based on intuition.\u00a0<\/p>\r\n

Successful brands use A\/B Testing as a constant process to guide their web development. However, since the introduction of tools like Optimizely<\/a> and VWO in 2010, A\/B testing has also become an important part of digital marketing.<\/p>\r\n

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How Does A\/B Testing Work?<\/h2>\r\n

In order to perform effective tests, you need a “hypothesis”, a way to edit your site, and a tool to record the results. Your\u00a0hypothesis<\/strong> is simply your idea about how to improve your webpage. This might be changing the location of a call-to-action, the layout of a page, or even the colour of a button.\u00a0<\/p>\r\n

\"what<\/p>\r\n

A\/B testing software<\/strong><\/a> monitors and records the effect of the changes on your visitors’ behaviour. It divides traffic between the ‘treatment’ and the ‘control’ and measures the different responses. More sophisticated tools even send more visitors to the best-performing page, so you don’t lost out on customers whilst your test is running.<\/p>\r\n

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Once your site has received enough visits, your software will declare a winner. However, there is another important step to make before the changes can be made permanent. Analysing the statistical significance<\/strong><\/a> of your data is a crucial part of the A\/B testing process.<\/p>\r\n

A\/B Testing In 30 Seconds (Animated Video)<\/h2>\r\n

As with any other kind of test, human error frequently interferes with otherwise convincing results. So, if you are considering performing A\/B tests on a website, make sure you avoid falling into the two most common mistakes:\u00a0<\/p>\r\n

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  1. The classic problem of confirming your own opinions<\/a>.<\/li>\r\n\t
  2. Trying to draw conclusions without having enough traffic, time or uplift (this is\u00a0the most common A\/B testing mistake<\/a>)<\/li>\r\n<\/ol>\r\n

    Whilst most A\/B testing tools<\/a> will do all the calculations for you, it is still worth taking some time to get familiar with A\/B testing statistics<\/a>.<\/p>\r\n