---
name: split-testing
description: Use when asked to compare two or more variations of a design/interface to see which performs better — the general step-by-step process (goal, variation, random allocation, data collection, analysis) shared with A/B testing (see a-b-testing), applied to UI/UX design decisions specifically.
---

# Split Testing

Split testing compares two or more variations of a webpage, interface, or
marketing element to determine which performs better against a specific
goal — the same underlying method as [[a-b-testing]], commonly
used as the name for this technique when applied specifically to design/
UI variations rather than broader product experiments.

## The general process

1. **Goal identification** — define the specific, measurable outcome
   being optimized (click-through rate, conversion rate, engagement).
2. **Variations creation** — build two or more versions of the element
   under test.
3. **Random allocation** — assign users (or a subset) randomly to each
   variation, ensuring a fair distribution and minimizing bias.
4. **User exposure** — each user interacts with exactly one variation.
5. **Data collection** — capture interaction and behavior data per
   variation against the chosen goal metric.
6. **Statistical analysis** — determine whether the observed difference
   between variations is statistically significant, not just noise.

## Relationship to A/B testing

"Split testing" and "A/B testing" describe the same core method; the
distinction in common usage is more about context than mechanism — split
testing often implies a design/marketing framing (which page layout
converts better), while [[a-b-testing]] is often the term used in a
broader product/feature-experimentation context. The statistical
discipline (pre-registered goal, random allocation, significance testing)
is identical either way.

## Common pitfalls

Shared directly with [[a-b-testing]]: stopping the test as soon as
one variation looks ahead (inflates false positives), choosing the goal
metric after seeing results (invites cherry-picking), and letting sample
pollution (a user seeing more than one variation) break the random-
allocation assumption the statistical analysis depends on.

## Learn more

- [[a-b-testing]] for the same underlying method in fuller depth.
- [[ui-ux-testing]] for the broader design-evaluation context split testing is commonly used within.