{"id":110602,"date":"2025-05-23T13:04:02","date_gmt":"2025-05-23T20:04:02","guid":{"rendered":"https:\/\/thegood.com\/?post_type=insights&p=110602"},"modified":"2025-07-09T08:35:32","modified_gmt":"2025-07-09T15:35:32","slug":"why-rapid-test","status":"publish","type":"insights","link":"https:\/\/thegood.com\/insights\/why-rapid-test\/","title":{"rendered":"How To Make User-Centered Decisions When A\/B Testing Doesn\u2019t Make Sense"},"content":{"rendered":"\n

The right tool for the right job. It’s a principle that applies everywhere, from construction sites to surgical suites, yet for digital product development, many teams are singularly focused on A\/B testing.<\/p>\n\n\n\n

Don’t get me wrong, A\/B testing is incredibly powerful. It’s the gold standard for high-stakes, high-traffic decisions where statistical significance matters most. But when it becomes your only tool, you create unnecessary constraints that can paralyze decision-making and slow innovation<\/a>.<\/p>\n\n\n\n

The reality is that different decisions require different levels of rigor, confidence, and investment. Luckily, there is a complementary approach that fills critical gaps in your experimentation toolkit. By understanding when each method is most appropriate, teams can make faster, more informed optimizations while maintaining the rigor needed for their most high-stakes decisions.<\/p>\n\n\n\n

Creating \u201cexperience rot\u201d<\/h2>\n\n\n\n

A\/B testing borrowed its methodology from medical intervention studies, where 95% confidence intervals and statistical significance aren’t just nice-to-haves; they’re life-or-death requirements.<\/p>\n\n\n\n

But we’re not rocket scientists, and we don\u2019t always need the same level of assurance in product decisions to move towards the right outcome.<\/p>\n\n\n\n

\"An<\/figure>\n\n\n\n

A\/B testing can be overkill for the decisions product teams need to make daily. Yet teams have become so committed to this single methodology that they’ve created what researcher Jared Spool calls “Experience Rot<\/a>,\u201d the gradual deterioration of user experience quality from teams moving too slowly or focusing solely on economic outcomes.<\/p>\n\n\n\n

The costs of slow testing cycles<\/a> are tangible and measurable:<\/p>\n\n\n\n