{"id":99239,"date":"2025-06-25T13:29:39","date_gmt":"2025-06-25T07:59:39","guid":{"rendered":"https:\/\/vwo.com\/blog\/?p=99239"},"modified":"2025-08-11T17:30:19","modified_gmt":"2025-08-11T12:00:19","slug":"are-your-a-b-test-results-giving-you-the-complete-story-a-breakdown","status":"publish","type":"post","link":"https:\/\/vwo.com\/blog\/are-your-a-b-test-results-giving-you-the-complete-story-a-breakdown\/","title":{"rendered":"Are Your A\/B Test Results Giving You The Complete Story? A Breakdown"},"content":{"rendered":"\n

You spend hours upon hours planning an A\/B test.<\/p>\n\n\n\n

Coming up with a solid hypothesis, getting buy-in from stakeholders, creating new designs for the variation, and ensuring everything is in place to execute it.<\/p>\n\n\n\n

Finally, you launch the test and eagerly wait to know how visitors interact with it.<\/p>\n\n\n\n

The numbers slowly roll in. But it\u2019s not what you hoped. <\/p>\n\n\n\n

Test reports indicate that the control ultimately outperformed the variation.<\/p>\n\n\n\n

And just like that, all your hard work has led to a possible \u2018failed\u2019 test.<\/p>\n\n\n\n

This is a recurring scenario for every experimentation team. And when it happens, most of them just scrap the hypothesis and move on to the next one.<\/p>\n\n\n\n

But are you missing a crucial stage in this optimization cycle<\/a>?<\/p>\n\n\n

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

Your reports are just numbers<\/strong><\/h2>\n\n\n

A\/B test reports tell you how many visitors were part of your experiment, the number of conversions, and the percentage of uplift or drop your test recorded.      <\/p>\n\n\n\n

You take one look at the report, and you instantly know whether your test was successful or not. <\/p>\n\n\n\n

And that\u2019s about it. <\/p>\n\n\n\n

The report does not provide any additional information about why your test performed the way it did.<\/p>\n\n\n\n

You don\u2019t know which factors influenced visitor actions, whether any glitch impacted their experience, or if they got distracted by something else on the page.     <\/p>\n\n\n\n

So, even though reports give you a clear indication of how the test performed, you still won\u2019t know the context or reason for it.<\/p>\n\n\n\n

That\u2019s because you are only looking at the numbers or the quantitative side of data.<\/p>\n\n\n\n

Pro Tip!<\/strong>

Use VWO Copilot Editor to instantly generate custom variations for your test. Just select the section you want to tweak, enter a prompt, and let the AI-powered tool create multiple options to test against your control. Skip the tedious process of creating each variation manually, and streamline your workflow in a few clicks.<\/p><\/div><\/div><\/div><\/div>\n\n\n

The \u2018What\u2019 vs \u2018Why\u2019: A never-ending debate of two data types<\/strong><\/h2>\n\n\n

When it comes to research and optimization, you usually deal with two types of data<\/a>.<\/p>\n\n\n

Quantitative data (The \u2018What\u2019)<\/strong><\/h3>\n\n\n

This data type includes metrics that can be defined by numerical values.\u00a0<\/p>\n\n\n\n

For instance, conversion rate, bounce rate, click-through rate, page views, average order value, and so on.<\/p>\n\n\n\n

Based on this quantitative data, you can answer some key questions:<\/p>\n\n\n\n