One counts votes on a picture. The other measures what people do with their money. Here is when to reach for each, and how to move a winning design onto your live site.
Search for A/B testing in Figma and you will mostly find plugins that show two frames to a group of people and count which one they prefer. Those are preference tests. They are genuinely useful, and they are not A/B tests.
An A/B test splits your live traffic between two versions of a real page, keeps everything else constant, and measures which version produces more signups, orders, or revenue. The two methods answer different questions, and the trouble starts when a preference result gets reported as a conversion result.
| Figma preference test | Live A/B test | |
|---|---|---|
| Runs on | A static frame or prototype | Your actual website |
| Who participates | Teammates, stakeholders, panel voters | Real visitors already trying to buy |
| What is measured | Which design people say they prefer | Whether behaviour changed: signups, orders, revenue |
| Control group | None | Your current page, running at the same time |
| Confidence | Raw vote counts | Statistical significance on a defined goal |
| Time to a result | Hours | Days to weeks, depending on traffic |
| What you get at the end | A decision meeting | A shipped page and a number |
A preference vote asks someone to step outside the buying process and judge a design. That is a different mental state from someone comparing your price to a competitor's on a Tuesday afternoon.
Three things push the results apart:
The failure mode to watch for. A redesign wins a preference vote 8 to 2, gets built, gets shipped without a test, and conversion drops. Nobody can say why, because there was never a control. That is not a Figma problem. It is a measurement problem that started when a vote got treated as evidence.
Plenty of times, honestly. Preference and usability testing on prototypes earns its place early in the process:
What matters is reporting it as what it is. "Seven of ten people preferred version B" is a fine sentence. "Version B won our A/B test" is not, if version B never met real traffic.
This used to be the expensive step, and it is the reason so many teams stop at the vote. Getting an approved Figma frame in front of real visitors meant a developer building it, which meant a ticket, a sprint, and a queue.
That is the part MidaGX removes. You open it on the live page you want to change, paste a link to the Figma frame, and it rebuilds the design as a test variant on your real site. You review it across desktop and mobile, attach a conversion goal, and split your traffic. The full workflow is on the Figma A/B testing page.
The practical sequence, then, looks like this:
No. They are good at what they do, which is helping a team choose between design directions quickly and cheaply. The problem is only when a preference result gets presented as a conversion result.
Not in a way that predicts revenue. A prototype has no real traffic, no pricing pressure, and no checkout, so there is nothing to measure conversion against. For that, the design has to run on the live site.
It depends on your baseline conversion rate and the size of the effect you are trying to detect. Our sample size calculator will give you a number for your own figures, and the significance calculator tells you whether a finished test actually proved anything.
That works the same way. If the Figma frame is a whole page, MidaGX rebuilds the whole page as the variant. Full redesigns are exactly the kind of change that deserves a test rather than a launch.
Paste a Figma link, launch the test, get a number instead of an argument. Free up to 100,000 monthly tested users.
Create a free accountSearch for A/B testing in Figma and you mostly find plugins that show two frames to a group of people and count which one they prefer. That is a useful design review tool. It is not an A/B test, and treating the result as one is how teams ship confident losers.
A preference vote samples teammates, stakeholders, and panel participants who know they are being asked. An A/B test samples the people already trying to buy from you.
A vote captures which design someone says they like. A test captures whether the design changed what they actually did: signed up, added to cart, checked out.
A vote has no control group and no significance testing. An A/B test holds everything else constant and tells you whether the difference is real or noise.
Preference testing is genuinely useful early, when you are narrowing directions and a live test would be a waste of traffic. The mistake is stopping there and calling the winner validated.
No. They are good at what they do, which is helping a team choose between design directions quickly. The problem is only when a preference result gets reported as a conversion result.
People are poor predictors of their own behaviour, and voters are not in a buying mindset. A design can look cleaner in a side-by-side comparison and still perform worse when someone is actually trying to complete a purchase.
Not in a way that predicts revenue. A prototype has no real traffic, no pricing pressure, and no checkout. To measure conversion you need the design running on the live site.
Paste the Figma frame link into MidaGX on the page you want to change. It rebuilds the design as a variant on your live site, then you set a goal and publish.
Mida.so is a super lightweight A/B testing tool to help you experiment, analyze and implement conversion strategies in minutes.
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