The multiple comparisons problem: Why running many A/B tests requires special care

You've spent weeks perfecting your A/B test. The results come in, and boom - statistical significance! Time to pop the champagne, right?

Not so fast. If you're running multiple tests at once (and let's be honest, who isn't?), you might be celebrating a mirage. The dirty secret of A/B testing is that the more tests you run, the more likely you are to find "winners" that are actually just statistical noise.

Understanding the multiple comparisons problem in A/B testing

Here's the thing about running multiple tests: each test you add increases your chances of a false positive. It's basic probability. Run one test with a 5% false positive rate? You've got a 5% chance of being wrong. Run 20 tests? Now you're looking at a 64% chance that at least one of those "significant" results is bogus.

Think of it like this - if you flip a coin enough times, you'll eventually get five heads in a row. Doesn't mean the coin is rigged. Same goes for your tests. The Reddit community gets pretty heated about this, and for good reason. Without proper corrections, you're basically p-hacking without realizing it.

The real cost isn't just statistical. You implement that "winning" variation, allocate resources to roll it out company-wide, and then... nothing. No real impact. Meanwhile, you've missed actual opportunities because you were chasing ghosts.

So what's the fix? You need multiple testing correction. The classics are:

• Bonferroni correction: Super conservative, divides your significance level by the number of tests

• Benjamini-Hochberg procedure: More balanced, controls false discovery rate instead of family-wise error

Both have their place, but honestly? Most teams overcorrect and end up with tests that never reach significance. There's a sweet spot between being too trigger-happy and being paralyzed by statistical rigor.

How interference affects simultaneous A/B tests

Running multiple tests at once creates another headache: interference. Your tests start messing with each other like noisy neighbors. User sees variation A from test 1 and variation B from test 2 - now which one caused that conversion?

Some teams try to solve this by isolating tests completely. Different user segments, different pages, whatever. But Georgi Georgiev makes a solid point - you end up shipping combinations you never actually tested. That's like cooking a meal where you taste each ingredient separately but never try the final dish.

The paranoia about test interactions might be overblown though. Microsoft's experimentation team ran the numbers and found that harmful interactions are pretty rare. They happen, sure, but not nearly as often as people think.

Here's what actually helps:

• Limit concurrent tests to what you can realistically monitor

• Pick your battles - not every metric needs protection

• Use a platform that handles correction methods automatically

The goal isn't perfection. It's finding the right balance between moving fast and not fooling yourself with bad data.

Statistical techniques for controlling errors in multiple tests

Let's get practical about fixing this mess. You've got two main approaches, and picking the right one depends on what keeps you up at night.

If false positives terrify you, go with Bonferroni correction. It's the nuclear option - divides your p-value threshold by the number of tests. Running 10 tests? Your new significance level is 0.005 instead of 0.05. Brutal, but effective.

If you care more about finding real effects, the Benjamini-Hochberg procedure is your friend. Instead of controlling family-wise error rate, it controls false discovery rate. Translation: you accept that some percentage of your "discoveries" will be false, but you keep that percentage reasonable.

But here's what most guides skip - you need statistical power too. All the correction in the world won't help if your tests are underpowered. Before you even start:

1. Run a power analysis

2. Figure out your minimum detectable effect

3. Calculate the sample size you actually need

4. Accept that some tests just aren't worth running

The teams that win at this game use tools like Statsig to automate these calculations. Because let's face it - manually adjusting p-values for every test is a recipe for mistakes.

Best practices for effective A/B testing with multiple comparisons

Time for the real talk. Most A/B testing failures happen before you even launch the test. Data dredging - where you fish around for significant results after the fact - kills more experiments than any statistical error.

Start with clear hypotheses. Not "let's see what happens" but "we think X will increase Y by Z%". Write them down. Share them with your team. Make them embarrassingly specific. This discipline alone will cut your false positive rate in half.

When it comes to running tests, you've got three options:

• Sequential testing: One at a time, super clean, super slow

• Parallel with corrections: Multiple tests, statistical adjustments, moderate speed

• YOLO mode: Test everything, pray for the best (spoiler: this ends badly)

Most successful teams land somewhere in the middle. They group related metrics together, apply corrections to the groups, and accept that some interactions might slip through.

Communication is huge here. When you share results, be upfront about your corrections. "We found a 12% lift, p=0.03 after Benjamini-Hochberg correction for 5 simultaneous tests" builds way more trust than "We found a winner!"

One last thing - document everything. Which correction method? How many tests? What was included or excluded? Future you will thank present you when someone asks why that "successful" test from last quarter didn't move the needle.

Closing thoughts

Running multiple A/B tests without corrections is like driving without insurance - you might be fine, but when things go wrong, they go really wrong. The good news? This problem is completely solvable with the right approach and tools.

Start small. Pick one correction method, apply it consistently, and see what happens. You'll probably find fewer "winners" at first, but the ones you do find will actually work.

Want to dive deeper? Check out:

• Statsig's guide to multiple hypothesis testing

• Microsoft's research on A/B test interactions

• Your platform's documentation on statistical corrections

Hope you find this useful! Now go forth and test responsibly.