Experimentation Best Practices

Running experiments isn’t just about flipping switches and hoping for the best. The difference between experiments that generate real insights and those that waste time often comes down to a few key practices. Here’s what we’ve learned from working with thousands of teams on their experimentation programs.

Start with a Strong Hypothesis

Your experiment is only as good as the hypothesis behind it. A weak hypothesis leads to inconclusive results, even when everything else is done perfectly.

The best hypotheses follow a simple structure: “If [change], then [outcome], because [reasoning].” For example: “If we add social proof badges to product pages, then conversion rate will increase by 5%, because users trust products that others have purchased.”

Why does this work? It forces you to think through not just what you’re testing, but why you expect it to work. That reasoning becomes crucial when you’re interpreting results later.

Get Your Sample Size Right

Nothing kills an experiment faster than realizing afterward that you didn’t have enough users to detect a meaningful difference. This is especially painful when you’ve spent weeks collecting data.

Before you start, calculate your required sample size using a power analysis. Consider your baseline conversion rate—lower rates need larger samples to detect the same percentage change. If you’re looking for a 5% improvement on a 2% baseline, you’ll need far more users than improving from 20% to 25%.

As a general rule, aim to detect changes of at least 2-5%. Smaller improvements are often not worth the implementation effort, and detecting them requires massive sample sizes.

Choose Metrics Carefully

Here’s where many experiments go wrong: trying to measure everything instead of focusing on what matters.

Pick 1-2 primary metrics. These should directly measure what your hypothesis predicts will change. If you’re testing a new checkout flow, your primary metric should be conversion rate, not page views.

But don’t stop there. Set up guardrail metrics to catch unintended consequences. If your new checkout flow increases conversions but doubles customer service complaints, you need to know that. Monitor key business metrics like revenue, retention, and satisfaction alongside your primary metrics.

Secondary metrics help you understand the “why” behind your results. If conversion goes up, are users spending more time on the page? Are they more likely to return? These insights guide your next experiments.

Avoid the Peeking Problem

It’s tempting to check results daily, especially when you’re excited about a test. But repeatedly checking for significance without adjusting your analysis can lead to false positives.

If you must peek (and we all do), either use sequential testing methods that account for multiple looks, or commit to your original test duration. The worst outcome is stopping an experiment early because you saw a promising spike that later disappears.

Think Beyond the Test Window

The best experiments teach you something about your users, not just about a specific feature. When results come in, dig deeper than just “variant A beat variant B.”

Look at different user segments. Did new users respond differently than returning ones? Were there differences by device or geography? These patterns often reveal insights that apply to future experiments.

Consider long-term effects too. Some changes show immediate benefits that fade over time as novelty wears off. Others take weeks to show their true impact as users adapt to new workflows.

Scale Your Learning

As your experimentation program matures, the real value comes from the cumulative learning across all your tests. Document what you learn from each experiment, even the “failed” ones. Failed experiments that contradict your intuition are often the most valuable—they reveal gaps in your understanding of user behavior.

Build a roadmap of related experiments. If testing a new headline increases signups, what happens when you test the entire landing page? Each experiment should inform the next, creating a virtuous cycle of learning.

Additional Resources

What is Product Experimentation? @https://mixpanel.com/blog/product-experimentation/ Mixpanel’s Guide to Product Analytics @https://mixpanel.com/content/guide-to-product-analytics/intro/