Experimentation anti-patterns: Common mistakes

You know that sinking feeling when you realize your A/B test results don't make any sense? I've been there more times than I'd like to admit. Last quarter, we ran what we thought was a straightforward experiment on our checkout flow, only to discover three weeks later that we'd been comparing apples to... well, nothing really, since we forgot to set up a proper control group.

If you've been running experiments for any length of time, you've probably stumbled into at least one of these traps yourself. The good news is that most experimentation failures follow predictable patterns - and once you know what to watch for, they're surprisingly easy to avoid.

Neglecting proper experimental design

Let's start with the basics. A poorly designed experiment is worse than no experiment at all because it gives you false confidence in bad data. I learned this the hard way when I first started running tests.

The most painful mistake? Not having a clear hypothesis. I used to think I could just throw changes at the wall and see what stuck. "Let's test this new button color" sounds reasonable until someone asks what you're actually trying to learn. Without a specific, measurable hypothesis, you end up collecting data that answers... nothing really. You need something concrete like "changing the CTA button from gray to orange will increase click-through rates by at least 10% for mobile users."

Control groups are another thing people skimp on, usually because they're worried about "wasting" traffic on the control. Here's the thing though - without a control group, you have no idea if that 15% conversion lift was from your brilliant new feature or just because it was payday week. The Statsig team writes extensively about this, and they're right: external factors can completely invalidate your results if you don't have a proper baseline.

Then there's sample size. I can't tell you how many times I've seen teams get excited about early results from 50 users. Small samples are seductive because they give you answers fast, but they're about as reliable as a weather forecast for next month. You need enough data to separate signal from noise - and that usually means waiting longer than feels comfortable.

The teams at DZone have documented similar patterns, noting that rushed experimental design often stems from pressure to ship quickly. But here's what I tell my team: a week spent on proper design saves you from a month of arguing about meaningless results.

Compromising data quality and integrity

Bad data is like cooking with spoiled ingredients - no amount of fancy analysis will make the final dish taste good. And yet, data quality often gets treated as an afterthought.

The most insidious problem is biased data collection. Maybe your tracking only fires for users with fast internet connections. Or perhaps your mobile app silently fails to log events when the battery is low. These aren't hypothetical - I've seen both happen. The result? Your "comprehensive" analysis only represents your happiest, most engaged users.

Data validation sounds boring, but it's saved my bacon more times than I can count. Simple checks catch big problems:

• Are all your events firing?

• Do the numbers add up? (Users who clicked "buy" should show up in the checkout funnel)

• Are there sudden spikes or drops that don't match real-world events?

One validation trick I love: have someone on your team manually go through the user flow while you watch the data come in. You'd be amazed what this catches.

Outliers are tricky beasts. Your instinct might be to toss them out - after all, that one user who made 500 purchases in an hour is clearly a bot, right? But wholesale removal of outliers can hide real user behavior. Power users exist. People do weird things. Instead of deletion, consider Winsorization - capping extreme values rather than removing them entirely. This preserves the signal that unusual behavior exists while preventing it from dominating your analysis.

Misapplying statistical methods

Statistics in experimentation is like seasoning in cooking - use it wrong and you'll ruin the whole dish. The worst part? Unlike oversalted soup, statistical errors often taste just fine until someone else points out what went wrong.

Peeking at results is the gateway drug of bad experimentation. You set up a two-week test, but after three days the results look amazing. Surely a quick peek won't hurt? Wrong. Every time you check your results mid-flight, you increase the chance of seeing a false positive. It's like flipping a coin until you get five heads in a row and declaring the coin is rigged. The Statsig blog has great examples of how peeking inflates error rates - sometimes dramatically.

Choosing the wrong statistical test is another classic. I once watched a team use a t-test to compare conversion rates (which are proportions, not means). The test ran, produced a p-value, everyone celebrated... and the results were completely meaningless. Match your test to your data:

• Comparing averages? T-test

• Comparing proportions? Z-test or chi-square

• Multiple groups? ANOVA or its variants

• Not sure? Ask someone who knows - seriously, it's worth the time

The multiple comparisons problem is subtle but devastating. Say you're testing 20 different metrics. Even if nothing changed, you'd expect one of them to show "significant" results just by chance. That's why techniques like Bonferroni correction exist. Yes, they make it harder to find significant results. That's the point - they prevent you from fooling yourself.

I've found that teams who understand these statistical pitfalls run better experiments overall. They're more patient, more skeptical of surprising results, and ultimately more successful at finding real improvements.

Overcoming cultural barriers to experimentation

Technical problems are easy compared to people problems. You can fix a broken tracking script in an afternoon, but changing how your organization thinks about experimentation? That's a multi-month journey.

Leadership reluctance is the elephant in the room. I've worked with executives who say they want data-driven decisions but then override test results because "it doesn't feel right." Harvard Business Review's research on online experiments shows this isn't unique - many leaders struggle to let go of intuition-based decision making. The solution isn't to fight them head-on. Instead, start small with low-risk experiments that align with their instincts. Build trust gradually. Show them how experimentation validates good ideas, not just shoots them down.

Cognitive biases affect all of us, even (especially?) data people. Confirmation bias makes you design experiments to prove what you already believe. The sunk cost fallacy keeps bad features alive because "we spent so much time building it." I've caught myself testing the untestable - running experiments where only one outcome would actually change our plans.

The antidote? Build a culture where being wrong is okay. Celebrate experiments that disprove popular ideas. Make "I don't know" an acceptable answer. Some teams I've worked with have "bias check" sessions before launching experiments, where they explicitly discuss what biases might affect their design.

Poor collaboration might be the most fixable problem on this list, yet it persists everywhere. Product teams design experiments without talking to engineers about technical constraints. Data scientists analyze results without understanding the business context. Engineers implement tracking without knowing what questions need answering. As documented in discussions about the experimentation gap, these silos create experiments that technically run but practically fail.

The fix is embarrassingly simple: get everyone in the same room (or Zoom). Before any experiment, have a quick meeting with someone from product, engineering, and data. You'll catch 90% of problems before they happen. Yes, it's one more meeting. Yes, it's worth it.

Closing thoughts

Running good experiments isn't rocket science, but it does require discipline. Most failures come from rushing - skipping proper design, ignoring data quality, misusing statistics, or not getting organizational buy-in. The patterns are predictable, which means they're preventable.

Start with one thing: pick your next experiment and do it right. Clear hypothesis, control group, adequate sample size, clean data, appropriate statistics, and team alignment. It'll take longer than winging it, but you'll actually learn something useful.

Want to dive deeper? Check out Statsig's experiment design guide or browse through common experimentation mistakes other teams have made. And if you catch yourself about to peek at interim results, step away from the dashboard. Future you will thank present you.

Hope you find this useful!