Common Reasons A/b Testing Fails

AB testing can easily manipulate decisions under the guise of being "data-driven" if they're not used correctly. Sometimes AB tests are used to go through the motions to validate predetermined decisions and signal to leadership that the company is "data-driven" more than they're used to actually determine the right decision. After all, it's tough to argue with "we ran an AB test!" It's ⚡️data science⚡️... It sounds good, right? But what's under the hood? Here are a few things that could be under the hood of a shiny, sparkly AB test that lacks statistics and substance: 1. Primary metrics not determined before starting the experiment. If you're choosing metrics that look good and support your argument after starting the experiment... 🚩 2. Not waiting for stat sig and making an impulsive decision🚩 AB tests can look pretty wild in the first few days... wait it out until you reach stat sig or the test stalls. A watched pot never boils. 3. Users not being split up randomly. This introduces bias in the experiment and can lead to Sample Mismatch Ratio which invalidates the results🚩 4. Not isolating changes. If you're changing a button color, adding a new feature, and adding a new product offering, how do you know which variable to attribute to the metric outcome?🚩 You don't. 5. User contamination. If a user sees both the control and the treatment or other experiments, they become contaminated and it becomes harder to interpret the results clearly. 🚩 6. Paying too much attention to secondary metrics. The more metrics you analyze, the more likely one will be stat sig by chance 🚩 If you determined them as secondary, treat them that way! 7. Choosing metrics not likely to reach a stat sig difference. This happens with metrics that likely won't change a lot from small changes (like expecting a small change to increase bottom funnel metrics, ex. conversion rates in SaaS companies)🚩 8. Not choosing metrics aligned with the change you're making and the business goal. If you're changing a button color, should you be measuring conversion or revenue 10 steps down the funnel?🚩 AB testing is really powerful when done well, but it can also be like a hamster on a wheel-- running but not getting anywhere new. Do you wanna run an AB test to make a decision or to look good in front of leadership?

Day 6 - CRO series Strategy development ➡  A/B Testing (Part 3) Common Pitfalls in A/B Testing (And How to Avoid Them) A/B testing can unlock powerful insights—but only if done right. Many businesses make critical mistakes that lead to misleading results and wasted effort. Here’s what to watch out for: 1. Testing Multiple Variables at Once If you change both a headline and a CTA button color, how do you know which caused the impact? Always test one variable at a time to isolate its true effect. 2. Using an Inadequate Sample Size Small sample sizes lead to random fluctuations instead of reliable trends. ◾ Use statistical significance calculators to determine the right sample size. ◾ Ensure your audience size is large enough to draw meaningful conclusions. 3. Ending Tests Too Early It’s tempting to stop a test the moment one variation seems to be winning. But early spikes in performance may not hold. ◾ Set a minimum duration for each test. ◾ Let it run until you reach statistical confidence. 4. Ignoring External Factors A/B test results can be influenced by: ◾ Seasonality (holiday traffic may differ from normal traffic). ◾ Active marketing campaigns. ◾ Industry trends or unexpected events. Always analyze results in context before making decisions. 5. Not Randomly Assigning Users If users aren’t randomly split between Version A and B, results may be biased. Most A/B testing tools handle randomization—use them properly. 6. Focusing Only on Short-Term Metrics Click-through rates might rise, but what about conversion rates or long-term engagement? Always consider: ◾ Immediate impact (CTR, sign-ups). ◾ Long-term effects (retention, revenue, lifetime value). 7. Running Tests Without a Clear Hypothesis A vague goal like “Let’s see what happens” won’t help. Instead, start with: ◾ A clear hypothesis (“Changing the CTA button color will increase sign-ups by 15%”). ◾ A measurable outcome to validate the test. 8. Overlooking User Experience Optimizing for conversions shouldn’t come at the cost of usability. ◾ Does a pop-up increase sign-ups but frustrate users? ◾ Does a new layout improve engagement but slow down the page? Balance performance with user satisfaction. 9. Misusing A/B Testing Tools If tracking isn’t set up correctly, your data will be flawed. ◾ Double-check that all elements are being tracked properly. ◾ Use A/B testing tools like Google Optimize, Optimizely, or VWO correctly. 10. Forgetting About Mobile Users What works on desktop may fail on mobile. ◾ Test separately for different devices. ◾ Optimize for mobile responsiveness, speed, and usability. Why This Matters ✔ More Accurate Insights → Reliable data leads to better decisions. ✔ Higher Conversions → Avoiding mistakes ensures real improvements. ✔ Better User Experience → Testing shouldn’t come at the expense of usability. ✔ Stronger Strategy → A/B testing is only valuable if done correctly. See you tomorrow!

You spend weeks designing a test, running it, analyzing results... Only to realize the data is too weak to make any decisions. It’s a common (and painful) mistake (also completely avoidable). Poor experimentation hygiene damages even the best ideas. Let’s break it down: 1. Define Success Before You Begin Your test should start with two things: → A clear hypothesis grounded in data or rationale. (No guessing!) → A primary metric that tells you whether your test worked. But the metric has to matter to the business and be closely tied to the test change. This is where most teams get tripped up. Choosing the right metric is as much art as science, without it, you’re just throwing darts in the dark. 2. Plan for Every Outcome Don’t wait until the test is over to decide what it means. Create an action plan before you launch: → If the test wins, what will you implement? →If it loses, what’s your fallback? → If it’s inconclusive, how will you move forward? By setting these rules upfront, you avoid “decision paralysis” or trying to spin the results to fit a narrative later. 3. Avoid the #1 Testing Mistake: Underpowered tests are the ENEMY of good experimentation. Here’s how to avoid them: → Know your baseline traffic and conversion rates. → Don’t test tiny changes on low-traffic pages. → Tag and wait if needed. If you don’t know how many people interact with an element, tag it and gather data for a week before testing. 4. Set Stopping Conditions Every test needs clear rules for when to stop. Decide: → How much traffic you need. → Your baseline conversion rate. → Your confidence threshold (e.g., 95%). Skipping this step is the quickest way to draw false conclusions. This takes discipline, planning, and focus to make testing work for you. My upcoming newsletter breaks down everything you need to know about avoiding common A/B testing pitfalls, setting clear metrics, and making decisions that move the needle. Don’t let bad tests cost you time and money. Subscribe now and get the full breakdown: https://lnkd.in/gepg23Bs

“I ran an experiment showing positive lift but didn’t see the results in the bottom line.” I think we’ve all had this experience: We set up a nice, clean A/B test to check the value of a feature or a creative. We get the results back: 5% lift, statistically significant. Nice! Champagne bottle pops, etc., etc. Since we got the win, we bake the 5% lift into our forecast for next quarter when the feature will roll out to the entire customer base and we sit back to watch the money roll in. But then, shockingly, we do not actually see that lift. When we look at our overall metrics we may see a very slight lift around when the feature got rolled out, but then it goes back down and it seems like it could just be noise anyway. Since we had baked our 5% lift into our forecast, and we definitely don’t have the 5% lift, we’re in trouble. What happened? The big issue here is that we didn’t consider uncertainty. When interpreting the results of our A/B test, we said “It’s a 5% lift, statistically significant” which implies something like “It’s definitely a 5% lift”. Unfortunately, this is not the right interpretation. The right interpretation is: “There was a statistically significant positive (i.e., >0) lift, with a mean estimate of 5%, but the experiment is consistent with a lift result ranging from 0.001% to 9.5%”. Because of well-known biases associated with this type of null-hypothesis testing, it’s most likely that the actual result was some very small positive lift, but our test just didn’t have enough statistical power to narrow the uncertainty bounds very much. So, what does this mean? When you’re doing any type of experimentation, you need to be looking at the uncertainty intervals from the test. You should never just report out the mean estimate from the test and say that’s “statistically significant”. Instead, you should always report out the range of metrics that are compatible with the experiment. When actually interpreting those results in a business context, you generally want to be conservative and assume the actual results will come in on the low end of the estimate from the test, or if it’s mission-critical then design a test with more statistical power to confirm the result. If you just look at the mean results from your test, you are highly likely to be led astray! You should always be looking first at the range of the uncertainty interval and only checking the mean last. To learn more about Recast, you can check us out here: https://lnkd.in/e7BKrBf4