A/B testing sounds easy. Show half your visitors version A and half version B, see which does better. The idea is easy. Doing it so that the result means something is harder, and small teams are especially likely to fool themselves, because everyone wants the new design to win.
These are the five mistakes I see most often.
1. Stopping when it looks good
This is the big one. You launch a test, check it on day three, and the new version is up 15 percent. Everyone's excited. You stop the test and ship it.
The trouble is that early results swing wildly. With small numbers, random noise looks like a big effect. If you check every day and stop the moment the result looks good, you will find a "winner" far more often than chance should allow, including when the two versions are identical.
What to do instead: decide how long to run before you start. Work out the sample you need from your current conversion rate and the smallest improvement you'd care about, and run until you have it. Several tools and free calculators will do the sum. If you can't resist looking, look, but don't act on it.
2. Not knowing what "success" means beforehand
If you track twelve metrics and one of them improved, it's tempting to say the test worked. With enough metrics something will always move.
What to do instead: pick one main metric before the test starts, and write it down. Add a couple of guard-rail metrics that tell you if you're breaking something, such as refund rate or page speed. Treat everything else as interesting but unproven, something to test next time.
3. Testing changes too small to matter
A button that is slightly bluer might lift conversion by a hair, but you'd need enormous traffic to detect it. A small team with a few thousand visitors a week will run a test for months and get an unclear answer.
What to do instead: test bigger ideas. A different offer, a shorter form, a rewritten headline that changes the message, a new order of steps. If your traffic is low, say so honestly and consider other evidence: user interviews, session recordings, or a before-and-after comparison, with its limits understood.
4. Slicing until something works
The overall result is flat, so someone looks at mobile users in one city who arrived from one campaign, and finds a lift. That's very likely noise. If you cut the data enough ways, some slice will look good.
What to do instead: decide on a few segments before the test, with a reason for each, and treat any other finding as a hypothesis for a follow-up test. When a segment result matters, run a fresh test on that segment.
5. Skipping the checks that tell you the test is broken
Tests fail quietly. The split might not be 50/50 because of a bug. One version might load more slowly. Tracking might be missing on one side. Two tests might overlap and contaminate each other. The result looks like a result, but it isn't.
What to do instead: check that the traffic split matches what you intended. A split of 52/48 on a large sample can be a red flag, and it's a common way to spot bugs. Check that both versions record events properly. Run through the page yourself on a phone and a laptop. Look at the numbers on day one, not to judge the winner, but to confirm the machinery is working.
And one habit that helps
Keep a simple log. For each test: the idea, the reason, the main metric, the dates, the result, and what you decided. A spreadsheet is fine. After a year you'll have a record of what you've learned, you won't run the same test twice by accident, and you'll notice which kinds of ideas tend to work for your customers.
You don't need a big experimentation platform to do any of this. You need a bit of discipline before the test starts, and honesty about the result when it ends. Sometimes the honest result is "no difference," and that's still worth knowing: it means you can keep the version that's cheaper to maintain and move on.