{"id":23122,"date":"2022-12-13T17:18:02","date_gmt":"2022-12-13T17:18:02","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/12\/13\/what-is-a-b-testing-the-complete-guide-from-beginner-topro\/"},"modified":"2022-12-13T17:18:02","modified_gmt":"2022-12-13T17:18:02","slug":"what-is-a-b-testing-the-complete-guide-from-beginner-topro","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/12\/13\/what-is-a-b-testing-the-complete-guide-from-beginner-topro\/","title":{"rendered":"What is A\/B Testing? The Complete Guide: From Beginner to\nPro"},"content":{"rendered":"<p> <a href=\"https:\/\/go.fiverr.com\/visit\/?bta=1052423&nci=17043\" Target=\"_Top\"><img loading=\"lazy\" decoding=\"async\" border=\"0\" src=\"https:\/\/fiverr.ck-cdn.com\/tn\/serve\/?cid=40081059\" loading=\"lazy\"  width=\"601\" height=\"201\"><\/a>\n<\/p>\n<p>\n  <strong>A\/B testing splits traffic 50\/50 between a control and a variation.<\/strong> A\/B split testing is a <a href=\"http:\/\/www.win-vector.com\/blog\/2015\/06\/i-do-not-believe-google-invented-the-term-ab-test\/\">new term for an old technique<\/a>&mdash;<a href=\"http:\/\/ai.stanford.edu\/~ronnyk\/2009controlledExperimentsOnTheWebSurvey.pdf\">controlled experimentation<\/a>.\n<\/p>\n<p>\n  Yet for all the content out there about it, people still test the wrong things and <a href=\"https:\/\/cxl.com\/blog\/12-ab-split-testing-mistakes-i-see-businesses-make-all-the-time\/\">run A\/B tests incorrectly<\/a>.\n<\/p>\n<p>\n  This guide will help you understand <strong>everything you need to get started with A\/B testing.<\/strong> You&rsquo;ll see the best ways to run tests, prioritize hypotheses, analyze results, and the best tools to experiment through A\/B testing.\n<\/p>\n<div>\n  Table of contents<\/p>\n<ul>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#how-ab-testing-works\">What is A\/B testing?<\/a>\n    <\/li>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#improve-ab-test-results\">How to improve A\/B test results<\/a>\n    <\/li>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#prioritize-ab-tests\">How to prioritize A\/B test hypotheses<\/a>\n    <\/li>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#how-long-run-ab-tests\">How long to run A\/B tests<\/a>\n    <\/li>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#how-set-up-ab-tests\">How to set up A\/B tests<\/a>\n    <\/li>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#how-analyze-ab-tests\">How to analyze A\/B test results<\/a>\n    <\/li>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#how-archive-ab-tests\">How to archive past A\/B tests<\/a>\n    <\/li>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#ab-testing-statistics\">A\/B testing statistics<\/a>\n    <\/li>\n<li>\n      <a href=\"https:\/\/cxl.com\/blog\/#ab-testing-tools\">How to do A\/B testing: tools and resources<\/a>\n    <\/li>\n<\/ul>\n<\/div>\n<p>What is A\/B testing?<\/p>\n<p>\n  <strong>A\/B testing<\/strong> is a experimentation process where two or more variants (A and B) are compared, in order to determine which variable is more effective.\n<\/p>\n<p>\n  When researchers test the efficacy of new drugs, they use a &ldquo;split test.&rdquo; In fact, most research experiments could be considered a &ldquo;split test,&rdquo; complete with <a href=\"https:\/\/cxl.com\/conversion-optimization\/from-data-to-test-hypotheses\/\">a hypothesis<\/a>, a control, a variation, and a statistically calculated result.\n<\/p>\n<p>\n  That&rsquo;s it. For example, if you ran a simple A\/B test, it would be a 50\/50 traffic split between the original page and a variation:\n<\/p>\n<div>\n  <img loading=\"lazy\" decoding=\"async\" width=\"568\" height=\"406\" src=\"https:\/\/cxl.com\/wp-content\/uploads\/2015\/12\/What-is-AB-Testing-568x406.jpg\" loading=\"lazy\" alt=\"example of a simple a\/b test that splits traffic equally between two pages.\">A\/B testing splits traffic 50\/50 between a control and a variation.\n<\/div>\n<p>\n  For <a href=\"https:\/\/cxl.com\/conversion-optimization\/\">conversion optimization<\/a>, the main difference is the variability of Internet traffic. In a lab, it&rsquo;s easier to control for external variables. Online, you can mitigate them, but it&rsquo;s difficult to create a purely controlled test.\n<\/p>\n<p>\n  In addition, testing new drugs requires an almost certain degree of accuracy. Lives are on the line. In technical terms, your period of &ldquo;exploration&rdquo; can be much longer, as you want to be damn sure that you don&rsquo;t commit a <a href=\"https:\/\/cxl.com\/blog\/statistical-power\/\">Type I error (false positive)<\/a>.\n<\/p>\n<p>\n  Online, the process for A\/B split-testing considers business goals. It weighs risk vs. reward, exploration vs. exploitation, science vs. business. Therefore, we view results through a different lens and make decisions differently than those running tests in a lab.\n<\/p>\n<p>\n  You can, of course, create more than two variations. Tests with more than two variations are known as A\/B\/n tests. If you have enough traffic, you can test as many variations as you like. Here&rsquo;s an example of an A\/B\/C\/D test, and how much traffic each variation is allocated:\n<\/p>\n<div>\n  <img loading=\"lazy\" decoding=\"async\" width=\"568\" height=\"406\" src=\"https:\/\/cxl.com\/wp-content\/uploads\/2015\/12\/ABCD-Test-568x406.jpg\" loading=\"lazy\" alt=\"an example of how an a\/b\/n test splits traffic among multiple pages.\">An A\/B\/n test splits traffic equally among a control and multiple page variations.\n<\/div>\n<p>\n  A\/B\/n tests are great for implementing more variations of the same hypothesis, but they require more traffic because they split it among more pages.\n<\/p>\n<p>\n  A\/B tests, while <a href=\"https:\/\/cxl.com\/blog\/learning-analyzing-experiments\/\">the most popular<\/a>, are just one type of online experiment. You can also run <a href=\"https:\/\/cxl.com\/blog\/multivariate-tests\/\">multivariate<\/a> and <a href=\"https:\/\/cxl.com\/blog\/bandit-tests\/\">bandit tests<\/a>.\n<\/p>\n<p>A\/B Testing, multivariate testing, and bandit algorithms: What&rsquo;s the Difference?<\/p>\n<p>\n  A\/B\/n tests are controlled experiments that run one or more variations against the original page. Results compare conversion rates among the variations based on a single change.\n<\/p>\n<p>\n  Multivariate tests test multiple versions of a page to isolate which attributes cause the largest impact. In other words, <strong>multivariate tests are like A\/B\/n tests in that they test an original against variations, but each variation contains different design elements.<\/strong> For example:\n<\/p>\n<div>\n  <img loading=\"lazy\" decoding=\"async\" width=\"568\" height=\"406\" src=\"https:\/\/cxl.com\/wp-content\/uploads\/2015\/12\/multivariatet-568x406.jpg\" loading=\"lazy\" alt=\"example of a multivariate test on a web page.\">\n<\/div>\n<p>\n  Each element has a specific impact and use case to help you get the most out of your site. Here&rsquo;s how:\n<\/p>\n<ul>\n<li>Use A\/B testing to determine the best layouts.\n  <\/li>\n<li>Use multivariate tests to polish layouts and ensure all elements interact well together.\n  <\/li>\n<\/ul>\n<p>\n  You need to a ton of traffic to the page you&rsquo;re testing before even considering multivariate testing. But if you have enough traffic, you should use both types of tests in <a href=\"https:\/\/cxl.com\/institute\/online-course\/building-scaling-optimization-program\/\">your optimization program<\/a>.\n<\/p>\n<p>\n  Most agencies prioritize A\/B testing because you&rsquo;re usually testing more significant changes (with bigger potential impacts), and because they&rsquo;re simpler to run. As Peep once said, &ldquo;Most top agencies that I&rsquo;ve talked to about this run ~10 A\/B tests for every 1 MVT.&rdquo;\n<\/p>\n<p>\n  <strong><a href=\"https:\/\/cxl.com\/bandit-tests\/\">Bandit algorithms<\/a> are A\/B\/n tests that update in real time based on the performance of each variation.<\/strong>\n<\/p>\n<p>\n  In essence, a bandit algorithm starts by sending traffic to two (or more) pages: the original and the variation(s). Then, to &ldquo;pull the winning slot machine arm more often,&rdquo; the algorithm updates based on which variation is &ldquo;winning.&rdquo; Eventually, the algorithm fully exploits the best option:\n<\/p>\n<div>\n  <img loading=\"lazy\" decoding=\"async\" width=\"568\" height=\"371\" src=\"https:\/\/cxl.com\/wp-content\/uploads\/2015\/12\/abvbandit-568x371.jpg\" loading=\"lazy\" alt=\"example of how a bandit algorithm gradually shifts traffic to the winning variation.\"><a href=\"http:\/\/conductrics.com\/balancing-earning-with-learning-bandits-and-adaptive-optimization\/\">(Image Source)<\/a>\n<\/div>\n<p>\n  One benefit of bandit testing is that bandits mitigate &ldquo;regret,&rdquo; which is the lost conversion opportunity you experience while testing a potentially worse variation. This chart from Google explains that very well:\n<\/p>\n<div>\n  <img loading=\"lazy\" decoding=\"async\" width=\"426\" height=\"426\" src=\"https:\/\/cxl.com\/wp-content\/uploads\/2015\/12\/mabFigure5-426x426-1.jpg\" loading=\"lazy\" alt=\"chart showing the lost conversions per day that result from a\/b testing.\"><a href=\"https:\/\/web.archive.org\/web\/20190813020602\/https:\/\/support.google.com\/analytics\/answer\/2844870?hl=en\">(Image Source)<\/a>\n<\/div>\n<p>\n  Bandits and A\/B\/n tests each have a purpose. In general, bandits are great for:\n<\/p>\n<ul>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/5-characteristics-high-converting-headlines\/\">Headlines<\/a> and short-term campaigns;\n  <\/li>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/marketing-automation-strategy\/\">Automation<\/a> for scale;\n  <\/li>\n<li>Targeting;\n  <\/li>\n<li>Blending optimization with <a href=\"https:\/\/cxl.com\/blog\/conversion-attribution-modeling\/\">attribution<\/a>.\n  <\/li>\n<\/ul>\n<p>\n  No matter what type of test you run, it&rsquo;s important to have a process that improves your chances of success. This means running more tests, winning more tests, and making bigger lifts.\n<\/p>\n<p>How to improve A\/B test results<\/p>\n<p>\n  Ignore blog posts that tell you &ldquo;99 Things You Can A\/B Test Right Now.&rdquo; They&rsquo;re a waste of time and traffic. A process will make you more money.\n<\/p>\n<p>\n  Some <a href=\"https:\/\/econsultancy.com\/reports\/conversion-rate-optimization-report\">74% of optimizers<\/a> with a structured approach to conversion also claim improved sales. Those without a structured approach stay in what Craig Sullivan calls the <a href=\"http:\/\/www.slideshare.net\/sullivac\/emetrics-london-the-ab-testing-hype-cycle\">&ldquo;Trough of Disillusionment.&rdquo;<\/a> (Unless their results are littered with false positives, which we&rsquo;ll get into later.)\n<\/p>\n<div>\n  <strong><a href=\"https:\/\/www.slideshare.net\/sullivac\/emetrics-london-the-ab-testing-hype-cycle\" title=\"eMetrics London - The AB Testing Hype Cycle\">eMetrics London &ndash; The AB Testing Hype Cycle<\/a><\/strong> from <strong><a href=\"https:\/\/www.slideshare.net\/sullivac\">Craig Sullivan<\/a><\/strong>\n<\/div>\n<p>\n  To simplify a winning process, the structure goes something like this:\n<\/p>\n<ol>\n<li>Research;\n  <\/li>\n<li>Prioritization;\n  <\/li>\n<li>Experimentation;\n  <\/li>\n<li>Analyze, learn, repeat.\n  <\/li>\n<\/ol>\n<p>Research: Getting data-driven insights<\/p>\n<p>\n  To begin optimization, you need to know what your users are doing and why.\n<\/p>\n<p>\n  Before you think about optimization and testing, however, solidify your high-level strategy and move down from there. So, think in this order:\n<\/p>\n<ol>\n<li>Define your business objectives.\n  <\/li>\n<li>Define your website goals.\n  <\/li>\n<li>Define your <a href=\"https:\/\/cxl.com\/blog\/key-performance-indicators-kpi\/\">Key Performance Indicators<\/a>.\n  <\/li>\n<li>Define your target metrics.\n  <\/li>\n<\/ol>\n<div>\n  <img loading=\"lazy\" decoding=\"async\" width=\"568\" height=\"31\" src=\"https:\/\/cxl.com\/wp-content\/uploads\/2015\/12\/key-performance-indicator-ab-testing-568x31.jpg\" loading=\"lazy\" alt=\"flow chart showing progress from business objectives to target metrics.\">\n<\/div>\n<p>\n  Once you know where you want to go, you&nbsp;can collect the data necessary to get there. To do this, we recommend the <a href=\"https:\/\/cxl.com\/blog\/how-to-come-up-with-more-winning-tests-using-data\/\">ResearchXL Framework<\/a>.\n<\/p>\n<p>\n  Here&rsquo;s the executive summary of the process we use at CXL:\n<\/p>\n<ol>\n<li>\n    <a href=\"https:\/\/cxl.com\/institute\/online-course\/conversion-optimization-audits\/\">Heuristic analysis<\/a>;\n  <\/li>\n<li>Technical analysis;\n  <\/li>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/web-analytics-analysis\/\">Web analytics analysis<\/a>;\n  <\/li>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/heat-maps\/\">Mouse-tracking analysis<\/a>;\n  <\/li>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/qualitative-research-guide\/\">Qualitative surveys<\/a>;\n  <\/li>\n<li>\n    <a href=\"https:\/\/cxl.com\/conversion-optimization\/user-testing\/\">User testing<\/a> and <a href=\"https:\/\/www.wynter.io\/features\/website-copy-testing\">copy<\/a> <a href=\"https:\/\/www.copytesting.com\/\">testing<\/a>.\n  <\/li>\n<\/ol>\n<p>\n  <strong>Heuristic analysis<\/strong> is about as close as we get to &ldquo;best practices.&rdquo; Even after years of experience, you still can&rsquo;t tell exactly what will work. But you can identify opportunity areas. As <a href=\"https:\/\/cxl.com\/best-practice-and-optimisation-experts\/\">Craig Sullivan puts it<\/a>:\n<\/p>\n<blockquote>\n<p>\n    My experience in observing and fixing things: These patterns do make me a better diagnostician, but they don&rsquo;t function as truths&mdash;they guide and inform my work, but they don&rsquo;t provide guarantees.\n  <\/p>\n<p>Craig Sullivan\n<\/p><\/blockquote>\n<p>\n  Humility is crucial. It also helps to have a framework. When doing heuristic analysis, we assess each page based on the following:\n<\/p>\n<ul>\n<li>Relevancy;\n  <\/li>\n<li>Clarity;\n  <\/li>\n<li>Value;\n  <\/li>\n<li>Friction;\n  <\/li>\n<li>Distraction.\n  <\/li>\n<\/ul>\n<p>\n  <strong>Technical analysis<\/strong> is an often-overlooked area. Bugs&mdash;if they&rsquo;re around&mdash;are a conversion killer. You may think your site works perfectly in terms of user experience and functionality. But does it work equally well with every browser and device? Probably not.\n<\/p>\n<p>\n  This is a low-hanging&mdash;and highly profitable&mdash;fruit. So, start by:\n<\/p>\n<ul>\n<li>Conducting cross-browser and cross-device testing.\n  <\/li>\n<li>Doing a&nbsp;<a href=\"https:\/\/cxl.com\/11-low-hanging-fruits-for-increasing-website-speed-and-conversions\/\">speed analysis.<\/a>\n  <\/li>\n<\/ul>\n<p>\n  <strong>Web analytics analysis<\/strong> is next. First thing&rsquo;s first: Make sure everything is working. (You&rsquo;d be surprised by how many analytics setups are broken.)\n<\/p>\n<p>\n  Google Analytics (and other analytics setups) are a course in themselves, so I&rsquo;ll leave you with some helpful links:\n<\/p>\n<ul>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/google-analytics-setup-101\/https:\/\/cxl.com\/blog\/google-analytics-setup-101\/\">Google Analytics 101: How to Set Up Google Analytics<\/a>;\n  <\/li>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/google-analytics-102\/\">Google Analytics 102: How To Set Up Goals, Segments &amp; Events in Google Analytics<\/a>.\n  <\/li>\n<\/ul>\n<p>\n  Next is <strong>mouse-tracking analysis<\/strong>, which includes <a href=\"https:\/\/cxl.com\/blog\/heat-maps\/\">heat maps, scroll maps, click maps<\/a>, <a href=\"https:\/\/cxl.com\/blog\/form-analytics\/\">form analytics<\/a>, and <a href=\"https:\/\/cxl.com\/blog\/session-replay-videos\/\">user session replays<\/a>. Don&rsquo;t get carried away with pretty visualizations of click maps. Make sure you&rsquo;re informing your larger goals with this step.\n<\/p>\n<p>\n  <strong>Qualitative research<\/strong> tells you the why that quantitative analysis misses. Many people think that qualitative analysis is &ldquo;softer&rdquo; or easier than quantitative, but it should be just as rigorous and can provide insights as important as those from analytics.\n<\/p>\n<p>\n  For <a href=\"https:\/\/cxl.com\/blog\/qualitative-research-guide\/\">qualitative research<\/a>, use things like:\n<\/p>\n<ul>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/on-site-surveys\/\">On-site surveys<\/a>;\n  <\/li>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/customer-surveys\/\">Customer surveys;<\/a>\n  <\/li>\n<li>\n    <a href=\"https:\/\/cxl.com\/blog\/customer-interviews\/\">Customer interviews<\/a> and focus groups.\n  <\/li>\n<\/ul>\n<p>\n  Finally there&rsquo;s&nbsp;<a href=\"https:\/\/cxl.com\/blog\/user-experience-testing\/\"><strong>user testing<\/strong><\/a>. The premise is simple: Observe how actual people use and interact with your website while they narrate their thought process aloud. Pay attention to what they say and what they experience.\n<\/p>\n<p>\n  With <a href=\"https:\/\/www.copytesting.com\/\">copy testing<\/a>, you learn how your actual target audience perceives the copy, what clear or unclear, what arguments they care about or not.\n<\/p>\n<p>\n  After thorough conversion research, you&rsquo;ll have lots of data. The next step is to prioritize that data for testing.\n<\/p>\n<p>How to prioritize A\/B test hypotheses<\/p>\n<p>\n  There are many frameworks to prioritize your A\/B tests, and you could even innovate with your own formula. Here&rsquo;s a way to prioritize work shared by <a href=\"https:\/\/twitter.com\/OptimiseOrDie\">Craig Sullivan<\/a>.\n<\/p>\n<p>\n  Once you go through all six steps, you will find issues&mdash;some severe, some minor. Allocate every finding into one of five buckets:\n<\/p>\n<ol>\n<li>\n    <strong>Test.<\/strong> This bucket is where you place stuff for testing.\n  <\/li>\n<li>\n    <strong>Instrument.<\/strong> This can involve fixing, adding, or improving tag\/event handling in analytics.\n  <\/li>\n<li>\n    <strong>Hypothesize.<\/strong> This is where you&rsquo;ve found a page, widget, or process that&rsquo;s not working well but doesn&rsquo;t reveal a clear solution.\n  <\/li>\n<li>\n    <strong>Just Do It.<\/strong> Here&rsquo;s the bucket for no-brainers. Just do it.\n  <\/li>\n<li>\n    <strong>Investigate.<\/strong> If an item is in this bucket, you need to ask questions or dig deeper.\n  <\/li>\n<\/ol>\n<p>\n  Rank each issue from 1 to 5 stars (1 = minor, 5 = critical). There are two criteria that are more important than others when giving a score:\n<\/p>\n<ol>\n<li>\n    <strong>Ease of implementation<\/strong> (time\/complexity\/risk). Sometimes, data tells you to build a feature that will take months to develop. Don&rsquo;t start there.\n  <\/li>\n<li>\n    <strong>Opportunity.<\/strong> Score issues subjectively based on how big a lift or change they may generate.\n  <\/li>\n<\/ol>\n<p>\n  Create a spreadsheet with all of your data. You&rsquo;ll have a prioritized testing roadmap.\n<\/p>\n<p>\n  We created <a href=\"https:\/\/cxl.com\/blog\/better-way-prioritize-ab-tests\/\">our own prioritization model<\/a> to weed out subjectivity (as possible). It&rsquo;s predicated on the need to bring data to the table. It&rsquo;s called PXL and looks like this:\n<\/p>\n<div>\n  <a href=\"https:\/\/cxl.com\/wp-content\/uploads\/2016\/09\/pxl.jpg\"><img decoding=\"async\" src=\"https:\/\/cxl.com\/wp-content\/uploads\/2016\/09\/pxl.jpg\" loading=\"lazy\" alt=\"example of a\/b testing prioritization framework.\" width=\"750\"><\/a>\n<\/div>\n<p>\n  <a href=\"https:\/\/docs.google.com\/spreadsheets\/d\/1DGuw1vkqYZ61plOpTcGHFDvh4MMP4kaRnlTtaXSWrJA\/edit?usp=sharing\">Grab your own copy of this spreadsheet template here<\/a>. Just click File &gt; Make a Copy to make it your own.\n<\/p>\n<p>\n  Instead of guessing what the impact might be, this framework asks you a set of questions about it:\n<\/p>\n<ul>\n<li>\n    <strong>Is the change above the fold<\/strong>? More people notice above-the-fold changes. Thus, those changes are more likely to have an impact.\n  <\/li>\n<li>\n    <strong>Is the change noticeable in under 5 seconds?<\/strong> Show a group of people the control and then the variation(s). Can they tell a difference after 5 seconds? If not, it&rsquo;s likely to have less of an impact.\n  <\/li>\n<li>\n    <strong>Does it add or remove anything?<\/strong> Bigger changes like removing distractions or adding key information tend to have more of an impact.\n  <\/li>\n<li>\n    <strong>Does the test run on high-traffic pages?<\/strong> An improvement to a high-traffic page generates bigger returns.\n  <\/li>\n<\/ul>\n<p>\n  Many potential test variables require data to prioritize your hypotheses. Weekly discussions that ask these four questions will help you prioritize testing based on data, not opinions:\n<\/p>\n<ol>\n<li>Is it addressing an issue discovered via user testing?\n  <\/li>\n<li>Is it addressing an issue discovered via qualitative feedback (surveys, polls, interviews)?\n  <\/li>\n<li>Is the hypothesis supported by mouse tracking, heat maps, or <a href=\"https:\/\/cxl.com\/blog\/visual-engagement-metrics\/\">eye tracking<\/a>?\n  <\/li>\n<li>Is it addressing insights found via digital analytics?\n  <\/li>\n<\/ol>\n<p>\n  We also put bounds on Ease of implementation by bracketing answers according to the estimated time. Ideally, a test developer is part of prioritization discussions.\n<\/p>\n<p>Grading PXL<\/p>\n<p>\n  We assume a binary scale: You have to choose one or the other. So, for most variables (unless otherwise noted), you choose either a 0 or a 1.\n<\/p>\n<p>\n  But we also want to weight variables based on importance&mdash;how noticeable the change is, if something is added\/removed, ease of implementation. For these variables, we specifically say how things change. For instance, on the Noticeability of the Change variable, you either mark it a 2 or a 0.\n<\/p>\n<p>Customizability<\/p>\n<p>\n  We built this model with the belief that you can and should customize variables based on what matters to your business.\n<\/p>\n<p>\n  For example, maybe you&rsquo;re working with a branding or user experience team, and hypotheses must conform to brand guidelines. Add it as a variable.\n<\/p>\n<p>\n  Maybe you&rsquo;re at a startup whose acquisition engine is fueled by SEO. Maybe your funding depends on that stream of customers. Add a category like, &ldquo;doesn&rsquo;t interfere with SEO,&rdquo; which might alter some headline or <a href=\"https:\/\/copytesting.com\/\">copy tests<\/a>.\n<\/p>\n<p>\n  All organizations operate under different assumptions. Customizing the template can account for them and optimize your optimization program.\n<\/p>\n<p>\n  Whichever framework you use, make it systematic and understandable to anyone on the team, as well as stakeholders.\n<\/p>\n<p>How long to run A\/B tests<\/p>\n<p>\n  First rule: <strong>Don&rsquo;t stop a test just because it reaches statistical significance.<\/strong> This is probably the most common error committed by beginner optimizers with good intentions.\n<\/p>\n<p>\n  If you call tests when you hit significance, you&rsquo;ll find that most lifts don&rsquo;t translate to increased revenue (that&rsquo;s the goal, after all). The <a href=\"https:\/\/cxl.com\/blog\/statistical-significance-does-not-equal-validity\/\">&ldquo;lifts&rdquo; were, in fact, imaginary<\/a>.\n<\/p>\n<p>\n  Consider this: When <a href=\"http:\/\/www.einarsen.no\/is-your-ab-testing-effort-just-chasing-statistical-ghosts\/\">1,000 A\/A tests<\/a> (two identical pages) were run:\n<\/p>\n<ul>\n<li>771 experiments out of 1,000 reached 90% significance at some point.\n  <\/li>\n<li>531 experiments out of 1,000 reached 95% significance at some point.\n  <\/li>\n<\/ul>\n<p>\n  Stopping tests at significance risks false positives and excludes <a href=\"https:\/\/cxl.com\/blog\/ab-test-validity-threats\/\">external validity threats<\/a>, like seasonality.\n<\/p>\n<p>\n  <strong>Predetermine a sample size and run the test for full weeks, usually at least two business cycles.<\/strong>\n<\/p>\n<p>\n  How do you predetermine sample size? There are lots of <a href=\"https:\/\/cxl.com\/ab-test-calculator\/\">great tools<\/a>. Here&rsquo;s how you&rsquo;d calculate your sample size with Evan Miller&rsquo;s tool:\n<\/p>\n<div>\n  <img loading=\"lazy\" decoding=\"async\" width=\"568\" height=\"347\" src=\"https:\/\/cxl.com\/wp-content\/uploads\/2015\/12\/samplesizecalc-568x347.jpg\" loading=\"lazy\" alt=\"example of sample size calculator for an a\/b test.\">\n<\/div>\n<p>\n  In this example, we told the tool that we have a 3% conversion rate and want to detect at least 10% uplift. The tool tells us that we need 51,486 visitors per variation before we can look at <a href=\"https:\/\/cxl.com\/blog\/magical-95-statistical-significance\/\">statistical significance<\/a> levels.\n<\/p>\n<p>\n  In addition to significance level, there&rsquo;s something called <a href=\"https:\/\/cxl.com\/blog\/statistical-power\/\">statistical power.<\/a> Statistical power attempts to <a href=\"https:\/\/grasshopper.com\/blog\/the-errors-of-ab-testing-your-conclusions-can-make-things-worse\/\">avoid Type II errors<\/a> (false negatives). In other words, it makes it more likely that you&rsquo;ll detect an effect if there actually was one.\n<\/p>\n<p>\n  For practical purposes, know that 80% power is the standard for <a href=\"https:\/\/cxl.com\/blog\/ab-testing-tools\/\">A\/B testing tools<\/a>. To reach such a level, you need either a large sample size, a large effect size,..\n<\/p>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><br \/>\n<br \/><iframe data-lazy=\"true\" data-src=\"https:\/\/www.fiverr.com\/gig_widgets?id=U2FsdGVkX18x7XQvttUTrv1oEqmGNGTgvvCUiUoJ\/AP4z\/UyMz8lXGOLpu15jIMxBbTR0gmD5uBoFvhC4KWeALQRp3h\/X\/AwcVD0K8Wj9H\/ZzYKzcCNHosB9oS4SCJJFWiN85P9ICAc4OgCoE\/wHKIY7CDkf2\/DQ1vqGvk4smVe5cRDEmrLPCWi4FC8p40VUhSmWQ5udCm0zoJtorgWv3vbDQw0kKYkwn39ozAnQXDe+YvWMxkLFWA+O3TFwkJvdkIK+\/AUSnRssPKt5WHY0FhNOxnSPcLslEL4G4\/RfP95ve99U+kRnDy3X+KtzdQLY+u935ghON\/o3UE4IMv9oN6JX9RnxzL\/LRcOgnHigxStSGPKsZYtnz8RWNVT\/rOLAibqiWJadC5MYHRbekF3eg6FOGrQGkXYbsn0+a5aovnlLCbLwIqY9fcS17UX8J235iQ6cdmHNbrPeS84CMm34RA==&affiliate_id=1052423&strip_google_tagmanager=true\" loading=\"lazy\" data-with-title=\"true\" class=\"fiverr_nga_frame\" frameborder=\"0\" height=\"350\" width=\"100%\" referrerpolicy=\"no-referrer-when-downgrade\" data-mode=\"random_gigs\" onload=\" var frame = this; var script = document.createElement('script'); script.addEventListener('load', function() { window.FW_SDK.register(frame); }); script.setAttribute('src', 'https:\/\/www.fiverr.com\/gig_widgets\/sdk'); document.body.appendChild(script); \" ><\/iframe>\n<br \/><a href=\"https:\/\/cxl.com\/blog\/ab-testing-guide\/\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A\/B testing splits traffic 50\/50 between a control and a variation. A\/B split testing is a new term for an old technique&mdash;controlled experimentation. Yet for&#8230;<\/p>\n","protected":false},"author":1,"featured_media":23123,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-23122","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-universe"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is A\/B Testing? The Complete Guide: From Beginner to Pro - mailinvest.blog<\/title>\n<meta name=\"description\" content=\"Technology is forever changing, and there are always new pieces of technology to replace obsolete ones. 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