--- title: The A/B Testing Process: Research, Prioritize, Test, Analyze description: A structured four-phase A/B testing process is outlined: Research, Prioritize, Test, and Analyze. The core argument is that most teams fail by skipping... canonical: https://daily.dev/posts/the-a-b-testing-process-research-prioritize-test-analyze-f8d1kxfpl twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The A/B Testing Process: Research, Prioritize, Test, Analyze | daily.dev og:description: A structured four-phase A/B testing process is outlined: Research, Prioritize, Test, and Analyze. The core argument is that most teams fail by skipping... og:url: https://daily.dev/posts/the-a-b-testing-process-research-prioritize-test-analyze-f8d1kxfpl og:image: https://api.daily.dev/og/posts/F8D1kXfPl.png og:image:alt: The A/B Testing Process: Research, Prioritize, Test, Analyze og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The A/B Testing Process: Research, Prioritize, Test, Analyze **[Medium](https://daily.dev/sources/medium_js)** · 8 min read · 0 upvotes · 0 comments ## Summary A structured four-phase A/B testing process is outlined: Research, Prioritize, Test, and Analyze. The core argument is that most teams fail by skipping straight to building tests without grounding them in research. Phase 1 covers building a research stack using heuristics, analytics, mouse-tracking, qualitative methods, and user testing. Phase 2 introduces a prioritization framework (PXL) and a three-bucket system to decide what to fix immediately, investigate further, or formally test. Phase 3 emphasizes writing falsifiable hypotheses and avoiding common pitfalls like stopping tests early or running contaminated simultaneous tests. Phase 4 stresses segmentation, statistical literacy, and archiving all results to build organizational experimentation memory. The key insight is that systematic testing compounds over time, while random testing hits local maxima. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the PXL framework used for in A/B test prioritization? PXL is a scoring framework for prioritizing which A/B tests to run, evaluating each potential test on objective criteria such as the strength of the evidence behind it, where the affected page falls in the funnel, and how easy the change is to implement, rather than relying on gut feel or stakeholder opinions. _Teams choosing what to test next can find prioritization frameworks like PXL discussed on daily.dev._ ### What is a good structure for writing an A/B test hypothesis? A strong hypothesis follows the structure: because we observed [evidence], we believe that [change] will cause [effect], which we will measure by [metric]. It should be falsifiable and rooted in actual research findings rather than a vague guess like believing a bigger button will increase clicks. _Analysts refining how they write testable hypotheses can explore experimentation practices on daily.dev._ ### Why might an A/B test show no overall lift but still be valuable? A test with no overall lift can still reveal a significant win among a specific segment, such as mobile users or new visitors, while a test that wins overall might actually be hurting the highest-value segment. Segmentation of results is described as the critical practice for correctly interpreting outcomes rather than judging tests as simply winners or losers. _Practitioners analyzing experiment results can track segmentation techniques and testing practices via daily.dev._ ## Similar posts on daily.dev - [Why Most A/B Tests Are Lying to You](https://daily.dev/posts/why-most-a-b-tests-are-lying-to-you-qelwpywxf) · Towards Data Science · 0 upvotes · 0 comments - [The AI era requires a different kind of experimentation.](https://daily.dev/posts/the-ai-era-requires-a-different-kind-of-experimentation--zypuzbx6t) · Elena's Growth Scoop · 1 upvotes · 0 comments - [Ecommerce Testing Strategy: Set Up Your Testing Process in 7 Steps](https://daily.dev/posts/ecommerce-testing-strategy-set-up-your-testing-process-in-7-steps-m9lkgsi74) · Netguru · 0 upvotes · 0 comments --- Tags: [#data-analysis](https://daily.dev/tags/data-analysis), [#product-management](https://daily.dev/tags/product-management), [#ab-testing](https://daily.dev/tags/ab-testing) [View this post on daily.dev](https://daily.dev/posts/the-a-b-testing-process-research-prioritize-test-analyze-f8d1kxfpl) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The A/B Testing Process: Research, Prioritize, Test, Analyze","url":"https://daily.dev/posts/the-a-b-testing-process-research-prioritize-test-analyze-f8d1kxfpl","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-a-b-testing-process-research-prioritize-test-analyze-f8d1kxfpl"},"datePublished":"2026-07-26T19:30:36.271Z","dateModified":"2026-09-14T07:03:34.217Z","description":"A structured four-phase A/B testing process is outlined: Research, Prioritize, Test, and Analyze. 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