A/B Testing Best Practices: The Complete Guide for 2025

A/B testing is one of the most powerful tools in a growth team's arsenal. But running tests without a proper methodology is like throwing darts blindfolded. Here's how to run tests that actually produce reliable, actionable results.

1. Start with a Hypothesis

Every good experiment starts with a clear hypothesis. Not "let's see what happens if we make the button bigger," but a structured statement:

"We believe that [change] will result in [outcome] because [reasoning]."

Example: "We believe that changing the CTA from 'Sign Up' to 'Start Free Trial' will increase signups by 15% because it reduces perceived commitment."

A good hypothesis is specific, measurable, and based on some insight about your users.

2. Calculate Sample Size First

Before you start, know how many visitors you need. Running a test with too few visitors leads to false positives. Use a sample size calculator with these inputs:

Baseline conversion rate — your current conversion rate

Minimum detectable effect — smallest improvement worth detecting (usually 10-20%)

Statistical power — typically 80%

Significance level — typically 95%

Rule of thumb: If your baseline is 5% conversion, you'll need ~3,000 visitors per variant to detect a 20% relative improvement.

3. Test One Variable at a Time

If you change the headline, button color, and image all at once, you won't know which change caused the result. Isolate variables:

✓ Good

Test A: Original headline
Test B: New headline
(Everything else identical)

✗ Bad

Test A: Original page
Test B: New headline + new image + new button

4. Wait for Statistical Significance

This is the hardest part. Resist the urge to call a winner early. You need:

95% confidence — only 5% chance the result is due to random chance

Minimum 1-2 full weeks — to account for day-of-week effects

Target sample size reached — as calculated before the test

Don't peek!

Checking results daily and stopping when you see significance inflates your false positive rate. Set a duration and stick to it.

5. Common Mistakes to Avoid

Stopping too early

Early results are unreliable. A test showing +50% after 100 visitors often regresses to 0% after 1,000.

Testing too many variants

Each variant needs the same sample size. 4 variants = 4x the traffic needed.

Ignoring segment effects

A change might hurt mobile users while helping desktop. Check segments before rolling out.

Testing low-impact changes

Button color tests rarely move the needle. Focus on headlines, value props, and pricing.

6. Pre-Launch Checklist

Before launching any test, verify:

Clear hypothesis documented

Sample size calculated

Test duration set (don't peek!)

Primary metric defined

Variant tested on all devices

Tracking verified working

Start Testing Today

A/B testing isn't rocket science, but it does require discipline. Follow these best practices, resist the urge to cut corners, and you'll build a reliable experimentation program that drives real growth.

The best time to start testing was yesterday. The second best time is today.