Common A/B Testing Mistakes Designers Make (And How to Avoid Them)

Most A/B tests "fail" because the test is poorly designed, not because the idea is bad.

You run a test, get unclear results, and can't make a decision. Or you get results that seem clear, but when you implement the "winner," nothing changes. The problem isn't your design—it's how you tested it.

The goal is clearer decisions and better design validation. Avoid these common A/B testing mistakes, and you'll get signals that actually help you make better design choices.

Here are 12 mistakes designers make, plus how to fix them.

A/B Testing Mistakes That Make Results Useless

When A/B test results are useless, it's usually because of two problems: confounded tests or unclear questions.

Confounded tests happen when you change multiple things at once. You can't tell which change caused the difference, so the results don't help you make decisions.

Unclear questions happen when you ask vague questions like "Which is better?" instead of specific questions like "Which headline is clearer?" Vague questions get vague answers.

What useful results look like:

• Clear question: You know exactly what you're testing and why
• One primary change: Only one variable differs between versions
• Right audience: You're testing with people who represent your target users
• Enough votes/traffic: You have enough data to make a decision (20-30+ votes minimum for directional signals)
• Documented learning: You know why you chose the winner and what you learned

If your results don't meet these criteria, they're probably not useful. Here's how to avoid the mistakes that create useless results.

12 Common A/B Testing Mistakes (And How to Fix Them)

1. Testing Multiple Variables at Once

Why it happens: You want to test everything quickly, so you change the headline, layout, and CTA all at once.

Fix:

• Test one variable at a time
• Keep everything else identical between versions
• If you need to test multiple things, run separate tests sequentially

Example: Instead of testing "Headline A + Layout A + CTA A" vs. "Headline B + Layout B + CTA B," test just the headline first. Once you have a winner, test the layout. Then test the CTA.

2. Asking Vague Questions ("Which Is Better?")

Why it happens: You want quick feedback, so you ask a simple question that doesn't specify what you're measuring.

Fix:

• Ask specific questions about what you're testing (clarity, trust, preference, task success)
• Write the question before you create the test
• Make it testable: "Which headline is clearer?" not "Which is better?"

Example: Instead of "Which do you like more?" ask "Which headline helps you understand what this product does faster?" or "Which CTA makes the next step more obvious?"

3. Changing the Goal Mid-Test

Why it happens: You start testing for clarity, but halfway through you realize you're actually testing for trust. Or you change what "success" means after you've started collecting votes.

Fix:

• Define the goal before you create the test
• Write down what success looks like (e.g., "60%+ preference for clarity")
• Stick to the goal throughout the test

Example: If you're testing headline clarity, don't switch to testing trust mid-test. Finish the clarity test, then run a separate trust test if needed.

4. Testing Aesthetics Before Clarity

Why it happens: You want the design to look good, so you test button colors or border radius before you've validated that people understand what you do.

Fix:

• Test clarity first (headline, value proposition, messaging)
• Then test trust (testimonials, logos, credibility signals)
• Then test action (CTAs, forms, onboarding)
• Save aesthetics for last (colors, spacing, polish)

Example: Don't test "rounded vs. square buttons" if people don't understand your value proposition yet. Fix clarity first, then optimize aesthetics.

5. Using the Wrong Audience

Why it happens: You test with friends, family, or your team because they're easy to reach, even though they don't represent your target users.

Fix:

• Test with people who match your target audience
• Use relevant communities, warm leads, or target users
• Only use friends/team for clarity checks, not preference tests

Example: If you're designing for B2B SaaS users, don't test with designers or random consumers. Test with B2B SaaS users or people who understand that context.

6. Stopping Too Early (Small Sample)

Why it happens: You get 5-10 votes and see a clear winner, so you stop and make a decision.

Fix:

• Get at least 20-30 votes minimum for directional signals
• More is better, but 20-30 is a good minimum
• If results are close (45-55%), get more votes or test again

Example: Don't make decisions after 5 votes. Wait for at least 20-30 votes. Small samples can be misleading—one strong opinion can skew results.

7. Ignoring Accessibility (Contrast, Readability)

Why it happens: You focus on aesthetics or preference without checking if the design is accessible or readable.

Fix:

• Check contrast ratios before testing (WCAG guidelines)
• Test readability, not just preference
• Fix accessibility problems before testing preferences

Example: Don't test "dark gray vs. light gray text" if one version has poor contrast. Fix accessibility first, then test preferences between accessible options.

8. Treating Small Differences as "Wins"

Why it happens: You see 52% vs. 48% and declare a winner, even though the difference is within margin of error.

Fix:

• Set a threshold for "clear winner" (e.g., 60%+ preference)
• If results are close (45-55%), treat them as equal or test again
• Don't overinterpret small differences

Example: If Version A gets 52% and Version B gets 48%, that's not a clear winner. Either test again, acknowledge both are equal, or choose based on other factors (clarity, trust, etc.).

9. Overfitting to One Test (No Repeat)

Why it happens: You run one test, get a winner, and never test it again or validate it with a different audience.

Fix:

• Test the same variable multiple times with different audiences
• Validate results with additional tests
• Don't treat one test as definitive proof

Example: If Headline A wins with designers, test it again with end users. If it wins again, you can be more confident. If it loses, you might need different messaging for different audiences.

10. Not Controlling Context (Mobile vs. Desktop)

Why it happens: You test on desktop but most users are on mobile, or you test without specifying device/context.

Fix:

• Specify the context (desktop, mobile, email, etc.)
• Test with the right context for your audience
• If context matters, test separately for each context

Example: If 70% of your users are on mobile, don't test only on desktop. Test mobile layouts, or at least specify that your test is for desktop and run a separate mobile test.

11. Confirmation Bias (Only Sharing Where You'll "Win")

Why it happens: You only share tests where you expect to win, or you only document successful tests, ignoring failures.

Fix:

• Share all tests, not just winners
• Document failures and learnings
• Learn from what doesn't work, not just what does

Example: If Headline B wins but you preferred Headline A, document why B won and what you learned. Don't ignore the result because it doesn't match your preference.

12. Not Documenting Learnings / Next Steps

Why it happens: You run a test, get results, make a decision, but don't write down why you chose the winner or what you learned.

Fix:

• Document the test (link, question, vote percentages)
• Write down why you chose the winner
• Note key insights or patterns
• Define next steps (what to test next)

Example: "We chose Headline A (65% votes) because it tested better for clarity with first-time visitors. Test: [link]. Key insight: People said Version A was clearer because it focused on outcomes, not features. Next: Test CTA wording with winning headline."

Avoid these 12 mistakes, and you'll get clearer signals from your A/B tests.

A/B Test Best Practices (A Simple Checklist)

Follow this checklist to run clean, useful A/B tests:

• [ ] One variable rule: Only one thing differs between Version A and Version B
• [ ] Define success signal: What vote percentage or outcome = decision? (e.g., 60%+ preference)
• [ ] Write a specific question: "Which headline is clearer?" not "Which is better?"
• [ ] Choose the right audience: Test with people who match your target users
• [ ] Set a stop rule: How many votes do you need? (e.g., 30+ votes minimum)
• [ ] Screenshot/store both versions: Keep records of what you tested
• [ ] Control context: Specify device, channel, or situation (desktop, mobile, email, etc.)
• [ ] Check accessibility first: Ensure both versions meet basic accessibility standards
• [ ] Test clarity before aesthetics: Fix comprehension before optimizing visuals
• [ ] Get enough votes: Wait for at least 20-30 votes minimum before deciding
• [ ] Decide + document: Make the decision, then write down why and what you learned
• [ ] Iterate: Use results to inform the next test

Use this checklist before every test. It prevents most common mistakes and ensures you get useful results.

8 Ready-to-Use Test Questions Designers Can Steal

Here are 8 strong question templates you can copy and adapt:

1. "Which version makes the value proposition clearer in 5 seconds?" (Tests clarity and comprehension)

2. "Which CTA tells you what happens next?" (Tests clarity of action)

3. "Which layout feels more trustworthy to a first-time visitor?" (Tests trust and credibility)

4. "Which headline makes you want to learn more?" (Tests motivation and engagement)

5. "Which navigation helps you find [specific feature] faster?" (Tests task success and usability)

6. "Which pricing layout makes it easier to compare options?" (Tests clarity and comparability)

7. "Which hero image better matches the value proposition?" (Tests alignment and messaging)

8. "Which empty state makes you want to take action?" (Tests motivation and clarity)

Use these questions as templates. Adapt them to what you're testing, but keep them specific and testable.

How to Avoid These Mistakes Using DesignPick

DesignPick naturally enforces or supports A/B test best practices in several ways:

Two-Option Comparisons

DesignPick forces you to create exactly two versions, which prevents testing multiple variables at once. You can't accidentally create a confounded test.

Easy Sharing

DesignPick makes it easy to share tests with the right audience. Share in relevant communities, with warm leads, or with your network—without needing to set up complex tracking.

Quick Directional Votes

DesignPick gives you fast directional signals (25-50+ votes in hours), which helps you avoid stopping too early. You can get enough votes quickly without waiting for production traffic.

Repeatable Tests

DesignPick makes it easy to run the same test multiple times with different audiences, which helps you avoid overfitting to one test. Test with designers, then test with end users.

Mini Workflow: Running a Clean DesignPick Test

Step 1: Choose one decision

Pick one variable to test. One headline. One CTA. One layout. Not multiple things.

Step 2: Create A and B

Design both versions. Keep everything identical except the one variable you're testing.

Step 3: Write a clear question

Use one of the question templates above, or write your own specific, testable question.

Step 4: Share to the right audience

Share with people who match your target users. Use relevant communities, warm leads, or your network.

Step 5: Set a vote threshold

Agree on how many votes you need (e.g., 30+ votes minimum). Wait for enough votes before deciding.

Step 6: Decide + document

Once you have enough votes, make the decision. Document why you chose the winner, include the test link, and note key insights.

Step 7: Iterate

Use the results to inform your next test. Test the next variable, or validate the winner with a different audience.

This workflow helps you avoid common mistakes and get clearer signals from your tests.

Ready to Run a Clean A/B Test?

Avoid these common mistakes, and you'll get clearer signals from your design experiments. Pick one variable, write a clear question, and run a test on DesignPick.

Upload both versions side-by-side, share with the right audience, and get real votes on which one works better. You'll have results in hours—fast enough to inform your next design decision.

Create your first test →

The Bottom Line

Most A/B tests fail because the test is poorly designed, not because the idea is bad. Avoid these 12 common mistakes: testing multiple variables, asking vague questions, using the wrong audience, stopping too early, and more.

Follow A/B test best practices: test one variable at a time, write specific questions, choose the right audience, get enough votes, and document your learnings. Use DesignPick to enforce these practices and get clearer signals from your design experiments.

Better tests lead to better decisions. Avoid these mistakes, and you'll get results that actually help you make better design choices.

Want more A/B testing strategies? Browse more posts on the blog.