Ever wondered why some experiments give surprising results, or why certain product changes don't have the impact you expected? Bias in experimentation might be the culprit. Bias can creep into experiments in sneaky ways, distorting results and leading you down the wrong path.

In this blog, we'll dive into the different types of bias that can affect your experiments, how to spot them, and strategies to minimize their impact. Let's explore how understanding and mitigating bias can help you run more effective experiments and make better, data-driven decisions.

Understanding bias in experimentation

Bias is like that sneaky little force that can mess up your experiment results without you even noticing. It can pop up anywhere in the research process—from how you collect data to how you analyze and interpret it. For instance, think about weight loss studies where participants who don't shed pounds decide to drop out. This creates what's called attrition bias, and suddenly your results are skewed. Bias in research is a real issue that can lead us to incorrect conclusions.

That's why spotting and tackling bias is so important if we want our research findings to be solid and reliable. In the world of machine learning, especially natural language processing, models can pick up on and amplify societal biases present in their training data. This kind of bias in machine learning can have big implications. Also, scientist bias—where personal beliefs or funding sources influence outcomes—can mess with how trustworthy our research is, as discussed in this Reddit thread.

We all know that online experiments can lead to major improvements, but putting rigorous experimentation practices into action isn't always easy for organizations. One way to detect bias is by looking at how selected candidates perform—this can uncover hidden prejudices in selection processes. Plus, using techniques like outlier capping and focusing on proximate metrics can help reduce variance in experiments, boosting throughput, as explored in The Experimentation Gap.

You might worry about running multiple A/B tests at the same time, thinking they could interfere with each other. But actually, interactions are rare and usually not a big deal. On the other hand, pre-experiment bias can be a problem when users in different groups behave differently even before any changes are made. This can throw off your results. Thankfully, Statsig addresses this by checking pre-experiment values for all metrics across experiment groups and giving you a heads up if there are significant differences.

Finally, A/B testing is your best friend when it comes to getting rid of selection bias. By randomly assigning users to treatment and control groups, you can be confident that any differences you see are actually due to the intervention you're testing. This helps you get accurate measurements and real causal insights, which are crucial for making informed decisions and not wasting time on ideas that don't work.

Types of bias that can affect experiments

So, what kinds of bias should we watch out for in our experiments? There are several, and they can sneak up on you if you're not careful.

First up is information bias. This happens when the variables in your study are inaccurately measured or classified, maybe because of self-reporting errors or poor interviewing techniques. This kind of bias can lead to flawed data and wrong conclusions. Here's an ELI5 explanation on Reddit that goes into more detail.

Then there's selection bias, which occurs when your study sample doesn't represent the population you're interested in. This can happen if, say, only certain types of people choose to participate in your study. As a result, your findings might not actually reflect the reality for the whole population. More on that here.

We also have cognitive biases like confirmation bias, where researchers might favor data that supports their existing beliefs. This can lead to interpreting results in a way that backs up pre-existing views, potentially skewing conclusions. Check out this discussion on scientist bias.

Other biases to keep in mind include:

• Interviewer bias: The way an interviewer behaves or asks questions can influence how participants respond.

• Publication bias: There's often a tendency to publish only positive results, ignoring negative or inconclusive findings.

So how do we tackle these biases? Strategies like double-blind studies, randomization, and standardization are super helpful. These methods help ensure that your evaluation is as objective as possible, leading to more accurate and reliable results.

Strategies to detect and mitigate bias

So, how can we find and reduce bias in our experiments? There are several strategies we can use.

Randomization and double-blind studies are fantastic tools to cut down on bias from both participants and researchers. By randomly assigning participants and keeping them (and sometimes even the researchers) unaware of which group they're in, we minimize selection and observer biases.

Then we've got statistical techniques like CUPED (Controlled Experiments Using Pre-Existing Data), which can help identify and adjust for pre-experiment biases. Statsig's Pulse calculation method does something similar, proactively detecting biases and alerting you when there are significant differences between control and test groups.

Don't underestimate the power of peer reviews and audits, either. Getting fresh eyes on your work can uncover biases that your internal team might have missed. External checks like these help maintain the integrity and reproducibility of your research. There's a great discussion on scientist bias that highlights this point.

Of course, A/B testing is the gold standard when it comes to establishing causality and eliminating selection bias. By randomly assigning users to different groups, you ensure that any differences observed are due to the intervention itself.

When traditional A/B tests aren't practical, quasi-experiments can be a good alternative. They aren't randomized, but they can help estimate counterfactuals. Techniques like linear regression with fixed effects and difference-in-difference modeling can mitigate biases in these situations.

Best practices for unbiased experimentation

To keep bias at bay, it's essential to establish clear hypotheses and success metrics right from the start. Define exactly what you're testing and how you'll measure success. Using balanced metrics helps support or refute your hypotheses fairly. This approach helps you avoid confirmation bias, where you might interpret results to fit what you already believe. There's more on confirmation bias here.

Conducting premortem exercises is another great practice. By imagining possible failures before they happen, you can identify potential biases and flaws in your experiment design. This proactive approach aligns with the importance of having trustworthy data in your experiments.

Also, be sure to avoid p-hacking and data dredging by sticking to your pre-established metrics and hypotheses. Changing metrics mid-experiment can compromise your results. Remember, selective reporting can lead to biased conclusions and poor decisions.

It's important to report all your results, whether they're positive or negative. Sharing the full picture helps mitigate reporting bias and builds trust in your findings. Plus, it allows you to learn from what didn't work and improve future experiments. There's a good discussion on reporting bias here.

Finally, make it a habit to regularly challenge your assumptions. Question how you're interpreting data, test out alternative hypotheses, and seek out different perspectives. This practice enhances your experimentation process's integrity and leads to better-informed decisions.

Closing thoughts

Understanding and addressing bias in experimentation is crucial for getting accurate and reliable results. By being aware of the different types of bias and implementing strategies to mitigate them, you can make better decisions based on your data. Tools like Statsig can help you detect biases and run more effective experiments.

If you're interested in learning more about unbiased experimentation and how Statsig can support your efforts, be sure to check out our other resources and guides. Happy experimenting!