What is an experimental group?

Ever wondered how scientists determine if a new drug really works or how companies decide between two website designs? It's all about experimentation, and at the heart of these experiments are experimental groups. Understanding how these groups function is key to making informed, data-driven decisions.

Whether you're conducting clinical trials, running A/B tests on your website, or experimenting with new product features, knowing how to set up and analyze experimental groups can make all the difference. In this blog, we'll dive into what experimental groups are, why they're important, and how you can effectively use them—especially with tools like Statsig. Let's get started!

Understanding experimental groups in experimentation

In scientific experiments, the experimental group is the group that gets the treatment or intervention we're testing (source). This group is compared to the control group, which doesn't receive the treatment, to see how the independent variable affects the outcome (source). The experimental group is key to validating our hypotheses and drawing meaningful conclusions from the experiment.

When we're designing an experiment, it's vital to make sure that the only difference between the control and experimental groups is the independent variable we're testing (source). This way, we can confidently attribute any changes in the dependent variable to our manipulation. If we don't control for other variables, we might end up with confounding factors that mess up our results (source).

To ensure our experiments are as reliable as possible, we often use a true experimental design, where participants are randomly assigned to either the experimental or control group (source). Randomization helps minimize selection bias and ensures that any differences we observe are due to the treatment itself. On the flip side, a quasi-experimental design doesn't use random assignment, which can introduce biases and limit how generalizable the results are.

In the world of online experiments, techniques like A/B testing leverage experimental groups to assess potential improvements in digital products (source). By comparing how the experimental group (those exposed to a new feature or design) performs against the control group (those using the existing version), businesses can make data-driven decisions and continuously optimize their offerings. This approach has been widely adopted across industries, leading to significant revenue gains and increased competitiveness (source).

But conducting experiments isn't just about setting them up—it's also crucial to ensure data quality and integrity (source). Techniques like A/A testing and automated checks help validate the experimentation system and boost confidence in the results. Additionally, using experiment parameters directly in code, instead of hardcoding group names, can enhance flexibility and simplify testing different variants (source). This is where platforms like Statsig come into play, providing tools to manage your experiments effectively.

Designing effective experiments with experimental groups

So, how do we design experiments that actually work? First off, we need to make sure that only one variable changes between your control and experimental groups. By keeping everything else the same, we can isolate the effect of our independent variable on the dependent variable. That's why control groups are so important—they give us a baseline to compare against.

Another key factor is randomization. Randomly assigning participants to either the control or experimental group helps eliminate selection bias. Plus, having a larger sample size reduces the impact of individual differences and makes our findings more reliable.

It's also important to choose the right type of experiment for your situation. In a true experimental design, we use random assignment, which generally gives us more reliable results. But sometimes, randomization isn't practical or possible. In those cases, we might use a quasi-experimental design, even though it can introduce biases.

In the digital realm, A/B testing is a staple for evaluating potential improvements. By comparing different versions and seeing which one performs better, businesses can make data-driven decisions that drive growth. However, it's essential to validate your experimentation systems and ensure data quality for trustworthy results.

And here's a pro tip: when implementing experiments in your code, use parameters instead of hardcoded group names. Platforms like Statsig recommend checking parameter values directly (source). This makes your code more flexible and easier to maintain, allowing you to test different variants quickly without altering the underlying code.

Implementing experimental groups in code

When it's time to bring experimental groups into your code, there's a handy tip: use parameters instead of hardcoded group names. This approach makes your code more flexible, allowing for dynamic evaluation of variables. Experimentation platforms like Statsig can help manage these configurations, simplifying the whole process.

By using parameters, you eliminate dependency on specific experiment group names. Your code becomes dynamic, with parameters acting as building blocks for different experimental setups. This means you can create and test any group configuration just by adjusting parameters in your experimentation platform—no need to rewrite code each time.

For instance, instead of hardcoding group names like "Sorted Long List" or "Sorted Short List," you can use experiment parameters directly in your code:

In this example, the parameters "sorted" and "length" determine how the search results behave. By tweaking these parameters in your experimentation platform, you can test different configurations without touching the code. It's a straightforward way to experiment quickly and efficiently.

Advanced experimentation techniques using experimental groups

Ready to take your experiments to the next level? Techniques like A/B/n tests, switchback experiments, and multi-armed bandits (MABs) can help optimize your product in sophisticated ways.

A/B/n tests allow you to compare multiple variants simultaneously, not just two. This is great when you have several ideas to test at once. Switchback experiments are ideal when there are significant network effects—like when changing variables for some users impacts all users. MABs strike a balance between exploration and exploitation, helping you optimize treatment delivery and personalize experiences based on context.

To handle more complex experimental setups, you might need advanced assignment strategies. Stratified sampling divides your population into homogeneous groups based on certain metrics or classifications, ensuring balanced representation. Configurable allocation duration lets users be enrolled in an experiment for only part of its duration—perfect for one-time interactions like signup flows. Persistent assignment saves users' enrollment states for ongoing experiments, so they experience consistency over time.

When deciding on the right experimental group setup for your specific use case, consider:

• Complexity: Use A/B/n tests for simple comparisons, and MABs or switchback experiments for more complex scenarios.

• Network effects: Choose switchback experiments when changing variables for some users affects all users.

• Optimization goals: Opt for MABs if you want to optimize treatment delivery and personalize experiences based on context.

Platforms like Statsig support these advanced techniques, allowing you to create experimental groups that drive meaningful insights (source). By leveraging the right experimental design for your needs, you can make data-driven decisions that truly improve your product and user experience.

Closing thoughts

Experimentation is at the heart of data-driven decision-making. By understanding and effectively implementing experimental groups, you can gain valuable insights that drive your product forward. Whether you're running simple A/B tests or diving into advanced techniques like multi-armed bandits, the key is to design your experiments thoughtfully and use the right tools—like Statsig—to manage them efficiently.

If you're eager to learn more, check out the Statsig documentation for deeper insights into experimentation best practices. Keep exploring, keep experimenting, and see how data can transform your decisions. Hope you found this helpful!