How to interpret experiment results accurately

Running experiments is at the heart of discovering what truly works. But interpreting those results? That's often where things get tricky. We've all been there—staring at data, wondering what it really means.

In this article, we'll dive into practical steps to make sense of your experimental data without getting lost in complex statistics. From setting clear objectives to avoiding common pitfalls, let's make the journey of data interpretation a bit smoother.

Establishing clear objectives and success criteria

Before you kick off any experiment, it's super important to define your primary and secondary metrics. Setting clear success criteria upfront helps guide your decisions later on and cuts down on the influence of cognitive biases when you're looking at the data. By thinking ahead about possible outcomes, you'll be in a better position to stay objective when analyzing the results.

Plus, having well-defined goals keeps you laser-focused on what's important. You'll collect data that really matters and won't get distracted by irrelevant stuff. A well-designed experiment, with clear objectives, is just more likely to give you insights that actually mean something.

And it's not just about you—setting success criteria helps you explain the purpose and expected outcomes to everyone else involved. When your team and stakeholders know what's up, they're more likely to get on board and help make the experiment a success. When everyone understands the goals, they can pitch in more effectively with the design and execution.

Thinking ahead to what might happen lets you build a solid analysis plan. By considering different scenarios, you can figure out exactly what data you'll need to back up your conclusions. This way, you avoid any gaps in your data collection and can interpret the results more confidently.

Collecting and preparing data for thorough analysis

Once your experiment is up and running, it's time to gather all the relevant quantitative and qualitative data. Bringing all this information together gives you a full picture of what's going on and can even uncover surprising insights for future experiments. But remember, ensuring data quality and integrity is key if you want to trust your conclusions.

To keep your data top-notch, think about implementing A/A tests to check your experimentation setup. These tests can spot any issues with your system, like invalid experiments or wonky formulas, so you know your results are accurate. And if you get any surprising results, be skeptical—that's Twyman's law for you: any figure that looks interesting or different is probably wrong.

Dealing with outliers, internet bots, and varied treatment effects is a must in online experiments. Bots can mess up your data, and outliers—like data from unusual accounts—can throw off your A/B tests. Sometimes, certain segments may show bigger effects that skew your overall results. For instance, Microsoft once had an experiment where a bug with Internet Explorer 7 users messed up their outcomes.

Also, watch out for carryover effects if you're reusing control and treatment groups—you might need to shuffle users between experiments. And keep an eye on your sample ratios. If you're aiming for a 50/50 split but end up with 50.2/49.8, it's important to address that to keep your data trustworthy.

Analyzing experimental data and avoiding common pitfalls

Now comes the tricky part—analyzing your data. You'll need a structured approach to make sure you're interpreting things accurately and not falling prey to cognitive biases. Statistical methods are your friend here; they'll help you figure out if your results are significant or just random chance. Misinterpreting data can lead to bad business decisions, but getting it right can offer some real value.

Being able to spot common patterns in your results—like the outright winner, outright loser, qualifying effect, clickbait effect, or flat result—can help validate your analysis. To avoid confirmation bias, it's a good idea to have others review your analysis. Sometimes a fresh pair of eyes can catch things you missed. And again, A/A tests can come in handy for checking your system and spotting false positives.

By the way, platforms like Statsig can really help when it comes to interpreting your results. For example, their Scorecard panel lets you compare primary and secondary metrics for each variant against the control. If you hover over a metric lift, you get all sorts of useful info—unit counts, means, totals, trend lines, raw data, and even projected impacts if you roll out the experiment completely.

Validating findings and challenging interpretations

Validating your findings is super important—and sometimes that means challenging your own interpretations. Getting your peers to review your analysis can give you fresh perspectives and help you avoid confirmation bias. Techniques like A/A testing can help verify that your experiment setup and data are accurate. And if you get surprising results, replicating them helps ensure they're real.

So, what are these A/A tests exactly? They're where you split users into two groups but give them the same experience. It helps you check if your platform is accurately reporting metrics and handling variance correctly. Interestingly, contrary to common belief, A/A tests can sometimes show statistically significant results just because of natural metric variance. This can lead to false positives if you're not careful.

If you get results that seem too good (or bad) to be true, it's a good idea to replicate the experiment. For example, Bing once reran a color experiment with 32 million users to confirm an unexpectedly positive result. By reproducing findings, you build confidence in your conclusions.

🤖💬 Related reading: The role of statistical significance in experimentation.

Closing thoughts

Interpreting experiment results doesn't have to be a daunting task. By setting clear objectives, collecting and preparing your data carefully, and analyzing it thoughtfully while avoiding common pitfalls, you can gain meaningful insights that drive your business forward. And remember, tools like Statsig are there to help make this process smoother.

If you're interested in learning more about experimental analysis and best practices, check out Statsig's resources on experimentation. Hope you found this helpful!