Statistics
The Bet Test: Spotting Problems in Bayesian A/B Test Analysis
Learn more
How to Reduce Type 1 Errors in A/B Testing (Explained)
Make every A/B test count. Discover proven techniques to reduce type 1 errors, maximize ROI on your experiments, and drive better business outcomes.
A/B testing holds the promise of data-driven product improvements, but without understanding the underlying statistics it can feel daunting.
The fear of a type 1 error — better known as a false positive — can especially loom large.
Did that promising uplift really mean something, or was it just a statistical fluke? Precious time and resources could be wasted chasing a change that doesn't deliver in the long term.
Or even worse, maybe a promising idea shows discouraging early results, and you mistakenly conclude it would have a harmful outcome. False positives also refer to erroneous negative results.
If that sounds confusing, just remember that Type 1 error refers to how we interpret the evidence against a “null hypothesis” (i.e., there is no measurable difference between our new idea and the “control” treatment).
If we get the impression that there is some meaningful difference in an experiment (good or bad) when really there is none, that is a false positive. A false negative, or Type 2 error, would be a failure to detect a meaningful difference when one actually exists.
Thankfully, there are proven ways to combat type 1 errors. By understanding these errors and adopting strategies like rigorous statistical methods and larger sample sizes, you can gain greater confidence in your A/B testing results.
In this guide, you'll discover how to reduce type 1 errors and ensure that solid, trustworthy data supports your product decisions.
We’ll cover:
What is a type 1 error?
Type 1 error examples
Common causes of type 1 errors in A/B testing
How to decrease type 1 errors
What is a type 1 error?
Imagine you're running an A/B test within your product. You want to see if a redesigned onboarding flow leads to more free trial conversions. The early data looks encouraging — the new version seems to be winning!
But be cautious. There's a potential pitfall lurking in the world of statistics: The type 1 error.
In simple terms, a type 1 error is a false positive. It's when your test results make it appear that a change you made had an impact, but in reality, the difference was just a matter of chance. It's like mistaking a flicker of good luck for a genuine improvement.
Bad luck could also cause type 1 errors. If your test results appear that a change had a negative impact when in reality the difference was just a matter of chance, that would also be a type 1 error.
Type 1 errors in A/B testing
In the world of A/B testing, type 1 errors can throw you off track. You might think you've hit upon a winning version of your new app layout, only to discover that when you roll out the change to everyone, the improvement disappears.
This is usually because your initial test results weren't built on strong enough statistical guarantees.
(That said, even the strongest guarantees aren’t 100% — a type 1 error could always appear by sheer chance alone. We’ll touch on this more in a bit…)
Consequences for business and product development
Type 1 errors aren't just a statistical nuisance; they can have real consequences for your business:
Misguided decisions: You might implement website changes that don't actually benefit users, or worse, might even harm the user experience.
Wasted resources: Time, money, and effort go into making changes that yield no real return on your investment.
Missed opportunities: While you're chasing false leads, you might overlook variations that actually have the potential to make a difference.
Type 1 error example in action
Here’s a specific scenario, demonstrating how a type 1 error can play out in the real world.
Let’s say you're working on a SaaS product with a free trial and have a hunch that changing the signup button from a neutral gray to a bold orange might boost your conversions. You run an A/B test with the two button colors as your variations.
Within the first week, the orange button version seems to surge ahead, with a 15% increase in conversions compared to the control. Encouraged by the results, you declare the orange button a winner and roll it out to all users.
However, over the following weeks, something strange happens:
The conversion rate steadily drops back down, eventually settling at a level nearly identical to what it was before the color change.
What happened? You likely encountered a type 1 error.
The initial jump in conversions wasn't a true indication of the orange button's superiority. Instead, it was likely due to random fluctuations in your data. Maybe a wave of particularly motivated visitors happened to arrive during the test's early days.
While the orange button might have caught their eye initially, the novelty wore off and the long-term conversion rate remained unaffected.
Common causes of type 1 errors in A/B testing
Knowing how type 1 errors happen is the first step to avoiding them. Let's break down some of the most frequent culprits:
Small sample sizes: Testing with too few visitors makes your data more prone to random swings. Imagine flipping a coin — getting heads five times in a row doesn't mean the coin always lands heads up. You need a much larger sample to get an accurate picture.
Peeking too early: It's tempting to check on your test results frequently, but if you jump the gun and stop a test before it's gathered enough data, you increase the risk of that misleading early trend turning out to be a fluke. (If you feel like peeking is going to happen anyway, you’ll want to reach for a Sequential statistical method to help put guardrails on your results.)
Multiple comparisons: If you run a bunch of tests at the same time, or keep tweaking your analysis after the fact, the chances of finding some statistically significant result purely by chance go up significantly.
Data dredging: This happens when you start with a vague idea, run your test, and then go hunting for any patterns or slices of data that show a positive result. If you look hard enough, you're bound to find some kind of pattern — even if it doesn't mean much.
How to reduce type 1 errors
While you can't completely eliminate the risk of false positives, there are proven strategies to minimize their chances of derailing your A/B testing efforts:
#1 Increase your sample size
A larger sample size provides a more accurate representation of your overall user population. This helps smooth out random fluctuations that could make it look like a variation is performing better or worse than it actually is. The more observations we can make, the more we can discern between “signal” and “noise.”
Before running a test, you’ll need to use a sample size calculator to determine how much traffic you'll need to achieve your desired level of statistical power and confidence.
#2 Set stricter significance levels
What are significance levels? In A/B testing, the significance level (or alpha level) represents the probability you're willing to accept of declaring a variation the winner when it actually had no effect. It's a measure of your tolerance for false positives (type 1 errors).
The standard significance level is 95%, meaning you accept a 5% chance of declaring a winner when the variation truly had no impact. For even greater certainty, consider setting a 99% significance level, reducing your risk of a false positive to 1%.
However, keep in mind this also means you'll need a larger sample size to achieve the same statistical power.
#3 Correct for multiple comparisons
When running multiple A/B tests simultaneously or reanalyzing data multiple times, the probability of encountering a false positive by chance increases substantially. Here are some methods to combat this:
Bonferroni correction
A simple method that divides your significance level by the number of comparisons you're making.
This approach is guaranteed to control the family-wise error rate (FWER), which means the probability of at least one false positive occurring across all your tests stays below your chosen significance level.
However, a major drawback of this method is it can be quite conservative, especially with many comparisons, potentially leading you to miss true effects.
More advanced techniques
Eppo’s experimentation analysis platform uses a slightly more nuanced version of Bonferroni correction, known as “preferential Bonferroni”. The distinction that makes it “preferential” is that it gives additional weight to the primary metric in your analysis, helping us avoid the slowdown in decision-making that the traditional Bonferroni approach may incur.
Other methods you may hear mentioned include Holm-Bonferroni or Benjamini-Hochberg procedures. While also controlling for type 1 errors, these methods are less strict than the Bonferroni correction and focus on p-values.
But what are p-values?
In hypothesis testing, a p-value tells you how likely it is that you'd observe your test results if there truly was no difference between your test variations. So, a smaller p-value means stronger evidence that there is a difference between your test variations.
Now let’s dive into the procedures:
Holm-Bonferroni procedure: This method compares p-values to a series of adjusted significance levels. The adjustment gets gradually less strict as you move through comparisons, providing more power than the Bonferroni correction at the beginning.
Benjamini-Hochberg procedure: This method focuses on controlling the false discovery rate (FDR), which is the expected proportion of false positives among your significant results.
It compares p-values against thresholds determined by the desired FDR and the number of tests being conducted, offering more power than both Bonferroni and Holm-Bonferroni.
#4 Use sequential testing methods
Sequential testing involves continuously analyzing your test data as it comes in. This allows you to stop the test early if there's overwhelming evidence in favor of (or against) a variation.
This method helps minimize type 1 errors caused by peeking — or prematurely declaring a winner based on early trends before your test is sufficiently powered.
How it works
Traditional A/B testing often relies on fixed sample sizes and predetermined test durations. This can lead to waiting for unnecessary data, especially if one variation quickly establishes a clear lead.
Sequential testing's continuous analyses can reveal conclusive results sooner, allowing you to act on that reliable data with confidence and stop the test before excessive data adds unnecessary noise.
#5 Ensure proper randomization
Randomly assigning visitors to variations is essential for eliminating any hidden biases in your test data. Factors like time of day, user device, or even previous website behavior could skew your results if randomization isn't done properly.
Make sure your A/B testing tool has robust randomization processes in place.
Next steps
Now that you've gained a solid understanding of how to minimize type 1 errors, you're ready to level up your experimentation practices.
That’s where Eppo comes in.
Eppo is a powerful experimentation and feature management platform designed to help companies make data-driven decisions with greater confidence.
With Eppo, you can:
Detect errors early: Eppo's suite of diagnostics alerts you to issues like sample ratio mismatch, ongoing data validity problems, and potential biases within your experiment setup. This allows you to fix issues before they invalidate your results.
Maximize statistical power: Use Eppo's built-in sample size calculators and sequential testing options. Sequential testing enables you to confidently stop tests as soon as statistically significant results are achieved, saving you time and effort.
Analyze with confidence: Eppo's flexible segmentation and advanced analysis features, coupled with statistical techniques like the Benjamini-Hochberg procedure, empower you to dissect complex datasets while controlling for false positives.
Ensure unbiased results: Eppo offers robust randomization capabilities to prevent hidden biases and confounding factors from skewing your experiment outcomes.
Foster trust and accountability: Eppo's commitment to statistical rigor and transparency allows you to share reliable experimental results across your team, encouraging data-driven decision-making and minimizing the risk of chasing false leads.
Eppo provides the tools and insights you need to conduct experiments with uncompromising accuracy. This leads to fewer type 1 errors in A/B testing, a more efficient experimentation process, higher-quality product improvements, and ultimately — more revenue and growth.
Ready to minimize your type 1 errors and experiment with confidence?
Make every A/B test count. Discover proven techniques to reduce type 1 errors, maximize ROI on your experiments, and drive better business outcomes.
Table of contents
Ready to go from knowledge to action?
Talk to our team of experts and see why companies like Twitch, DraftKings, and Perplexity use Eppo to power experimentation for every team.
Get a demo
