What is A/B Testing?
TL;DR
A/B testing (split testing) means showing two different versions of something to different users to see which performs better.
Example
Should your "Buy Now" button be green or blue?
Instead of guessing, you test it:
- 50% of visitors see a green button (Version A)
- 50% of visitors see a blue button (Version B)
- After 1,000 visitors, you compare which button got more clicks
If the green button got 3% clicks and the blue button got 4.5%, blue wins. Now you know, based on data, not opinion.
What you can A/B test:
- Headlines and copy
- Button colors, text, and placement
- Images and videos
- Page layouts
- Pricing displays
- Email subject lines
- Form length and fields
- Navigation menus
Explanation
How A/B Testing Works
- Hypothesis - "I think changing X will improve Y"
- Create variations - Make version A (control) and version B (variant)
- Split traffic - Show each version to a random subset of visitors
- Measure results - Track conversions, clicks, or other goals
- Analyze - Determine if the difference is statistically significant
- Implement winner - Roll out the winning version to everyone
Statistical Significance
The hardest part of A/B testing is knowing when you have enough data to trust the results.
If Version A has 2.1% conversion and Version B has 2.3%, is B really better? Or did you just get lucky with that batch of visitors?
Statistical significance (usually 95%) means you can be confident the difference is real, not random chance.
Common A/B Testing Mistakes
- Ending tests too early - Not enough data to be sure
- Testing too many things at once - You won't know what caused the change
- Testing tiny changes - Small tweaks often don't have measurable impact
- Ignoring segments - The winner overall might not be the winner for mobile users
Why It Matters
For Business Owners
Stop arguing about opinions. "I think blue is better" vs "I think green is better" goes nowhere. A/B testing provides data to settle the debate.
Compound improvements. A 10% improvement from one test, plus 10% from another, plus 10% from another... compounds into significant gains.
Reduce risk. Before making a big change, test it on a portion of your traffic first.
When to A/B Test
A/B testing requires enough traffic to get statistically significant results. If you have 100 visitors per month, you probably can't run meaningful tests.
As a rough guide: you need hundreds of conversions to test most things reliably.
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