You ran an A/B test. The results looked great. Version B showed a 12% lift in conversions with 95% statistical significance. You shipped it. Two weeks later, you check your numbers. Conversions haven't improved. In fact, they might have dropped.
What went wrong? You just experienced a Type 1 error, one of the most common and costly mistakes in A/B testing. And if you're running experiments without understanding Type 1 and Type 2 errors, you're probably making decisions that hurt your business without even knowing it.
This guide explains both error types in plain English, shows you exactly how they affect your A/B tests, and gives you practical ways to minimize them.
What Is a Type 1 Error?
A Type 1 error occurs when you conclude there's a difference between two things when there actually isn't one. In A/B testing terms: you declare a winner when there is no real winner.
Think of it like a fire alarm going off when there's no fire. The alarm (your test result) says something is happening, but nothing actually is.
In statistics, Type 1 error is also called:
- False positive
- Alpha error (α)
- "Rejecting the null hypothesis when it's true"
What Is a Type 2 Error?
A Type 2 error is the opposite: you conclude there's no difference when there actually is one. In A/B testing: you miss a real winner because your test said it wasn't significant.
This is like a fire alarm that doesn't go off when there IS a fire. The alarm stays silent, so you assume everything is fine, but your house is burning.
In statistics, Type 2 error is also called:
- False negative
- Beta error (β)
- "Failing to reject the null hypothesis when it's false"
Here's the Type 2 error scenario:
Type 2 errors are often overlooked because they're invisible. You never know you missed a winner. You just move on to the next test, unaware of the opportunity you left behind.
The relationship between these errors is a trade-off. If you try to eliminate Type 1 errors completely (by requiring 99.9% confidence), you'll dramatically increase Type 2 errors. You'll miss real winners because your threshold is too strict.
Why This Matters for Your Business
These aren't just statistical concepts. They translate directly into revenue.
The Cost of Type 1 Errors
Every false positive means:
- Development time wasted implementing a "winner" that isn't
- Potential conversion drop if the "winner" is actually slightly worse
- Lost opportunity cost from not testing something else
- Erosion of trust in your testing program
Real example: A major e-commerce company ran a test showing a new checkout flow improved conversions by 7%. They rolled it out site-wide. Three months later, a deeper analysis revealed the "lift" was actually random noise in the data. The new flow performed identically to the old one, but they'd spent six weeks of engineering time implementing and optimizing something that made no difference.
The Cost of Type 2 Errors
Every false negative means:
- Abandoning a variation that would have made you money
- Never knowing what you missed
- Potentially killing ideas that were actually good
Real example: An online retailer tested a simplified product page. The test ran for two weeks and showed no statistically significant difference. They abandoned the variation. Later, when they retested with a larger sample size, they found the simplified page actually converted 11% better. That's 11% more revenue they missed for months because they ended the first test too early.
The Math of Cumulative Errors
If you run 20 tests per year at 95% confidence, you should expect at least one false positive, statistically guaranteed. Run 50 tests? Expect 2-3 false positives.
This is why high-velocity testing programs need to be especially careful about error management. More tests means more chances for errors to slip through.
How to Minimize Type 1 Errors
1. Don't Peek at Results Early
The most common cause of Type 1 errors is "peeking" - checking your test results before it reaches the required sample size, seeing a promising trend, and calling it early.
Every time you peek and make a decision, you inflate your actual Type 1 error rate. If you peek 5 times during a test and stop when something looks significant, your real error rate might be 20-30%, not 5%.
Rule: Decide your sample size before the test starts. Don't look until you reach it.
2. Use Proper Sample Size Calculations
Running tests that are too small dramatically increases both error types. Use a sample size calculator before every test:
The smaller the effect you want to detect, the more visitors you need.
3. Pre-Register Your Hypothesis
Before running a test, write down:
- What you're testing
- What metric you're measuring
- What sample size you need
- When you'll check results
This prevents the temptation to change your success criteria after seeing the data, a major source of false positives.
4. Apply Multiple Testing Corrections
If you're testing multiple variations or multiple metrics, your Type 1 error rate compounds. Testing 5 variations? Your real error rate isn't 5%. It's closer to 23%.
Use Bonferroni correction or similar methods to adjust for multiple comparisons.
How to Minimize Type 2 Errors
1. Increase Sample Size
The single most effective way to reduce Type 2 errors is to run tests longer and collect more data. Larger samples mean more statistical power, which means you're more likely to detect real effects.
Most A/B tests are underpowered. The standard recommendation is 80% power (20% Type 2 error rate), but many tests in practice have power below 50%.
2. Test Bigger Changes
If you're testing tiny tweaks (button color, minor copy changes), the effect size will be small, and hard to detect without massive sample sizes.
Test meaningful changes: different value propositions, new page layouts, fundamentally different user flows. Larger effects are easier to detect.
3. Focus on High-Traffic Pages
Running tests on pages with 500 visitors per month means you'll need months to reach significance for anything but the largest effects. That's months of potential false negatives.
Prioritize tests on your highest-traffic pages where you can reach meaningful sample sizes quickly.
4. Consider Sequential Testing Methods
Traditional A/B testing requires you to wait until a fixed sample size before looking at results. Sequential testing methods (like those used in clinical trials) let you check results as data comes in while still controlling error rates.
This can help you detect winners faster and reduce the chance of giving up on a test too early.
The Modern Approach: Beyond Binary Pass/Fail
Traditional A/B testing forces a binary decision: winner or loser. This framework maximizes the impact of both Type 1 and Type 2 errors. You either ship a false positive or abandon a false negative.
Modern approaches like multi-armed bandits take a different philosophy. Instead of waiting for a definitive answer, they continuously allocate more traffic to better-performing variations while still exploring alternatives.
This reduces "regret," the cost of showing suboptimal variations, regardless of which error type might occur:
Common Mistakes Teams Make
Mistake 1: Only Caring About Type 1 Errors
Many teams obsess over avoiding false positives (because shipping a bad change is visible) while ignoring false negatives (because missed opportunities are invisible).
Both errors cost money. A 20% Type 2 error rate means you're missing 1 in 5 real winners.
Mistake 2: Using Arbitrary Confidence Levels
"95% confidence" is a convention, not a law of nature. Depending on your situation, 90% or even 80% confidence might be appropriate, especially if the cost of a false negative (missing a winner) outweighs the cost of a false positive (shipping a non-winner).
Mistake 3: Running Too Many Tests at Low Power
Some teams try to run dozens of tests simultaneously, each with tiny sample sizes. This leads to both more Type 1 errors (random noise looks like winners) and Type 2 errors (real effects get buried in the noise).
Fewer, better-powered tests beat many underpowered ones.
Mistake 4: Ignoring Practical Significance
Statistical significance isn't the same as practical significance. A test might show a "statistically significant" 0.3% lift, but if that lift isn't meaningful to your business, it doesn't matter whether it's a true effect or a Type 1 error.
Always ask: "Even if this result is real, is it worth implementing?"
FAQ: Type 1 and Type 2 Errors in A/B Testing
What is a Type 1 error in simple terms?
A Type 1 error means your test said there was a winner when there actually wasn't. You thought Version B was better, but it was really just random chance making it look that way.
What is a Type 2 error in simple terms?
A Type 2 error means your test said there was no winner when there actually was one. You had a better version, but your test couldn't detect it, usually because you didn't have enough data.
Which error is worse: Type 1 or Type 2?
Neither is universally worse. It depends on your situation. Type 1 errors waste resources implementing non-improvements. Type 2 errors miss real opportunities. Most teams underweight Type 2 errors because they're invisible.
How do you reduce Type 1 error in A/B testing?
Don't peek at results early. Calculate sample size before starting. Pre-register your hypothesis. Apply corrections for multiple testing. Use 95% confidence level or higher.
How do you reduce Type 2 error in A/B testing?
Run tests longer to collect more data. Test bigger, more meaningful changes. Focus on high-traffic pages. Aim for 80% statistical power minimum. Consider sequential testing methods.
What is an acceptable Type 1 error rate?
The standard is 5% (corresponding to 95% confidence). For high-stakes decisions, some teams use 1% (99% confidence). For exploratory tests, 10% (90% confidence) may be acceptable.
What is an acceptable Type 2 error rate?
The standard target is 20% (corresponding to 80% statistical power). This means you'll detect 80% of real effects. Higher power (90%+) requires larger sample sizes.
Can you have both Type 1 and Type 2 errors in the same test?
Not for the same conclusion. Each test result is either a false positive, false negative, true positive, or true negative. But across multiple tests, you'll inevitably have some of each error type.
What to Do Next
Understanding Type 1 and Type 2 errors transforms how you think about A/B testing. It's not about finding "winners." It's about making good decisions under uncertainty.
Start by auditing your current testing program:
- What's your real Type 1 error rate? (Are you peeking at results?)
- What's your statistical power? (Are your tests big enough?)
- How many potential winners are you missing?
Or skip the statistics entirely.
With Dalton AI, you don't interpret test results at all. There's no waiting for significance, no worrying about Type 1 or Type 2 errors, no binary winner-loser decisions to make.
Here's why: Dalton uses multi-armed bandits that continuously shift traffic toward better-performing variations while still exploring alternatives. The interpretation happens automatically, inside the system. You're never staring at a dashboard wondering "is this significant?" or "should I wait longer?"
The errors we've spent this entire article explaining? Dalton's approach sidesteps them. There's no moment where you declare a winner and risk a false positive. There's no test you end too early and miss a real improvement. Your website just keeps getting better, automatically, with every visitor contributing to the optimization.
That's what self-improving websites look like. No statistics degree required.