How to read and interpret experiment results accurately

Interpreting experiment results can sometimes feel like deciphering a complex puzzle. With so much data at our fingertips, it's easy to get lost in the numbers and miss the bigger picture. We've all been there—wondering if we've set the right goals or if we're making decisions based on solid evidence.

But it doesn't have to be that complicated! By setting clear objectives, collecting high-quality data, and analyzing results thoughtfully, you can unlock meaningful insights that drive real impact. Let's dive into how you can make sense of your experiment results with confidence.

Establishing clear objectives and success criteria

Before diving into any experiment, it's super important to set clear goals. Think of it like having a roadmap—it helps you know where you're headed and how to get there. Make sure you define your primary and secondary metrics before you kick off the experiment. This not only keeps those pesky cognitive biases at bay but also guides you on what data you need to collect.

And don't forget to loop in your stakeholders! Sharing these objectives helps everyone stay on the same page and sets clear expectations. Having well-defined success criteria is a game-changer. It tells you straight up whether your experiment hit the mark or not. Like Sadie Neve from Creative CX says, deciding on your outcomes beforehand leads to way more objective decisions when you're poring over the results.

Another bonus of setting clear goals? You know exactly what data to collect. Make sure you grab both quantitative and qualitative data—numbers give you the "what," but stories tell you the "why." This combo can reveal surprising insights and set you up for even more awesome experiments down the road.

Bottom line: setting clear objectives from the get-go lays down a solid foundation. It makes communication with your team and stakeholders smoother and boosts collaboration. Plus, when it's time to read the results, those well-defined success criteria make interpreting the data a whole lot easier.

Collecting and preparing high-quality data

To really understand what's going on in your experiment, you need to collect both quantitative and qualitative data. The numbers give you concrete insights, while the stories and feedback add valuable context. And before you jump into the real test, running an A/A test is a smart move—it helps you check your setup and catch any system glitches early on.

Keeping your data clean is key. That means dealing with outliers, bots, and varied treatment effects. Outliers can throw your numbers off, so you'll want to spot them and decide what to do about them. Bots? They can flood you with junk data, so make sure to filter them out. And don't forget—different groups might react differently to your experiment, so take that into account to avoid any misleading takeaways.

Getting your data into tip-top shape involves some good old data cleaning and preprocessing. You'll need to tackle missing values, make sure your formats are consistent, and double-check for any errors or weird inconsistencies. It's not the most glamorous part, but it's definitely worth it!

At the end of the day, collecting and prepping high-quality data sets you up for success. Putting in the effort here means your results will be accurate and actually mean something. It might take some meticulous work, but trust me, reliable data is totally worth it.

Analyzing experiment results effectively

Now comes the fun part—analyzing your results! But to get it right, you've got to be structured about it. Using statistical methods helps you figure out if your results are the real deal or just random chance. And sticking to these methods every time is key for making solid, data-backed decisions.

Watch out for traps like confirmation bias—it can sneak up on you and make you see what you want to see in the data. To dodge that, try getting a fresh pair of eyes on your analysis through peer reviews, double-check your findings with methods like A/A testing, and if something seems off, don't hesitate to run the experiment again.

Also, keep an eye out for common patterns in your results. Maybe you've got a clear winner or loser, or perhaps the effects only show up in certain groups, or sometimes, nothing changes at all. Spotting these patterns not only boosts your confidence in what the data is telling you but also points you in the right direction for future tests.

At Statsig, we believe applying statistical rigor is crucial when interpreting experiment results. Our platform helps you determine the significance of results, distinguishing true effects from random noise. When you combine statistical rigor with a keen eye on biases and solid validation, you're well on your way to accurately interpreting your experiment results. And that means you can make decisions based on real data—not just hunches.

Validating and interpreting findings with confidence

Validating what you've found is just as important as the analysis itself. Getting someone else to look over your work—like a peer review—can challenge your thinking and catch any biases you might have missed. Fresh eyes often see things you don't, leading to even better insights and ideas for new experiments.

If something in your results makes you go "huh?", it's time to rerun the experiment to see if you get the same outcome. Remember Bing's color experiment? They reran it with more users to make sure the findings were legit. Being a bit skeptical—like following Twyman's law (if it looks too good to be true, it probably is)—helps you spot any errors in surprising data.

Want to double-check your conclusions? Try these techniques:

• Run an A/A test to make sure your experiment setup is spot-on.

• Check that the user ratios line up with your experimental design—mismatched ratios can mess things up.

• Look out for outliers and fix any data collection hiccups.

Tools like Statsig can support you in running these validation tests and ensuring your experiment setup is accurate, making the process smoother. By taking these extra steps, you'll be a whole lot more confident in your findings. After all, the aim is to make smart, data-driven decisions that really move the needle for your business. Putting in the effort to rigorously validate and interpret your results is totally worth it.

Closing thoughts

Interpreting experiment results doesn't have to be a daunting task. By setting clear objectives, collecting high-quality data, analyzing thoughtfully, and validating your findings, you can make sense of your experiments with confidence. Remember, the goal is to make decisions that are grounded in reliable data and drive real impact.

If you're looking for more resources on how to effectively run and interpret experiments, check out Statsig's perspectives and our documentation. We've got plenty of tips and tools to help you along the way.

Hope you find this useful!