Understanding Confounding Variables

Most tests contain two factors—the independent and dependent variables. The independent is what you’re testing (like a new site layout), and the dependent is the outcome you measure (impact on ). These variables are also called the cause and effect.

A confounding variable is a third factor that can influence your experiment and lead you to incorrect conclusions.

Confounding variables complicate experiments as they affect both the independent and dependent variables. This relationship means you might see a strong connection between the cause and effect when, in reality, the confounding variable is pulling the strings.

Confounding variables in product and web experiments

In web and product , confounding variables can take many forms—seasonal trends, changes in , competitors' actions, and technical updates. Product teams must recognize and account for these potential presences in their experimental design and analysis.

Imagine you’re testing a new feature on your website to see if the change improves . The experiment seems straightforward until a major holiday occurs. changes, but not because of your new feature. The plummet because people are off work, busy with holiday activities, and are not on your website as much. In this case, the holiday is a confounding variable because you knew user engagement would change during this period.

Understanding confounding variables is essential because they can lead to false positives (thinking your change made a difference when it didn’t) or false negatives (missing a real effect because a confounding variable masked it).

Confounding variables vs. lurking variables

Confounding and lurking variables can both mess up the results of an experiment—but they’re not quite the same.

A lurking variable is a hidden variable that wasn’t considered in the study and affects the relationship between the independent and dependent variables. The variable usually influences the dependent variable but isn’t directly related to the independent variable. This impact means the factors can create hidden biases and variability in your results.

Lurking variables are not necessarily directly linked to what you’re studying but can create misleading associations.

Let’s say you are experimenting with a new recommendation algorithm on your . You find the amount of time users spend watching content increases—great.

• Confounding variable: You launched the algorithm just before the release of a highly anticipated new series on your platform. The increased viewing time might be due to the popular content rather than the effectiveness of the new algorithm.

• Lurking variable: Users’ internet speed. Users with faster internet connections may have a smoother streaming experience and spend more time watching content, regardless of the recommendation algorithm.

In this example, the lurking variable (internet speed) affects the dependent variable (time spent watching content) but isn’t linked to the independent variable (the new recommendation algorithm). Something like a user’s internet speed might be harder to identify as it has a more subtle influence on your results.

However, as the algorithm rollout and the new series release happened at the same time, we consider it a confounding variable. We knew the release was happening, and its impact could have been measured and accounted for.

Both variables can skew your results, but confounding variables are often more problematic because they lead to entirely wrong conclusions about cause and effect.

While important to identify, lurking variables don’t create the same false relationship—they mainly add “noise” to the data.

Confounding variables can significantly impact your experiments, often in ways that aren’t immediately obvious.

Distorted results

The most direct impact of confounding variables is that they can bend your experimental outcomes. Certain factors can exaggerate the effect of your changes, minimize or overlook them, and even reverse them entirely.

This distortion (or false conclusion) can lead you to make incorrect decisions about your product or website and take you down the wrong path. You might implement changes that don’t improve your product or miss out on refinement opportunities.

Wasted resources

Acting on results influenced by confounding variables means you risk investing time, money, and effort into changes that don’t benefit your users or your business.

These mistakes can be costly, especially for smaller companies or teams with limited resources.

Reduced trust in data

If your team discovers that confounding variables influenced past experiments, this can undermine the trust in your experimentation process.

This distrust might make stakeholders hesitant to rely on data for decision-making in the future.

Difficulty in replication

Experiments affected by confounding variables are often hard to replicate. Retesting can be frustrating and time-consuming, especially if you’re trying to verify results or build on previous findings.

Slower innovation

When you can’t trust your experimental results due to confounding variables, it can slow your ability to innovate and improve your product.

You might become overly cautious or spend too much time accounting for every possible variable.

Spotting confounding variables means picking apart your experiment to look for the factors that might influence your results.

The goal isn’t to eliminate all variables—that’s often impossible. Instead, it’s about recognizing and accounting for their existence in your analysis to ensure your conclusions are as reliable as possible.

Common examples

First, you need to be aware of the most common types of confounding variables. These factors are most likely to appear in general product or web experiments.

• User demographics: Age, gender, and location massively influence how users interact with your product. Younger users might be more receptive to new features, while those in different countries may have varying preferences due to .

• Time factors: The time of day, day of the week, or season can affect user behavior. Depending on your industry or product type, a feature might perform differently during work hours than on evenings or weekends.

• Device type: often exhibit different behavior. A change that works well on desktops might translate poorly to mobile devices. This point also applies to various browsers and operating systems.

• User experience level: New users may interact with your product differently than long-time users, distorting experiment results.

• Internal events: A new marketing campaign, pricing change, or system update can manipulate user interactions. Technical issues and bugs might cause harmful data.

• External events: Major news events, holidays, or competitors' actions can impact user behavior during your experiment.

Detection methods

Awareness is only half the challenge. You might know that a confounding variable could be present in your results but not know how to find it properly—here’s where a “detection method” comes in handy.

Correlation analysis

Use statistical tools to determine relationships between potential variables and your experiment outcomes. Are your results closely tied to any particular user characteristic or behavior? Strong may suggest a confounding variable.

For example, you might discover that the success of your new feature correlates with user age—this demographic factor could be a confounder.

Data visualization

Sometimes, a picture is worth a thousand words (or data points). help reveal patterns that raw numbers hide.

Plot your results against different variables. Unexpected clusters or trend lines can signal a confounding variable.

Pre-testing and pilot studies

Before you roll out your full experiment, run a smaller version. The pre-test can help you spot confounders before they impact your main event.

During your pilot, look for unforeseen results or behaviors—these can be red flags for confounding variables. This “practice run” enables you to debug your experiment design.

It’s always important to be mindful of confounding variables. However, there are certain testing situations where the factors are more likely to impact your experiments.

• Long-running experiments: Tests spanning weeks or months provide more opportunities for external factors to influence results, such as seasonal variations, internal business changes, user familiarity, and external events.

• Cross-platform tests: Conducting experiments across different devices or platforms may introduce unique confounding variables. For instance, a mobile app update could coincide with your web experiment and affect user behavior.

• Simultaneous changes: Testing several changes at once (like a website design and recommendation algorithm) makes it harder to isolate their effects. You can’t attribute changes in user engagement to either change specifically.

• Complex user journeys: Tests with multiple steps or user journey touchpoints present more chances for confounding variables to creep in.

• Diverse user segments: Experimenting on diverse user bases (e.g., demographics, behaviors, experience level, or preferences) may result in wildly different reactions to experimental changes, introducing confounding effects.

• New feature launches: requires extra consideration. Factors like novelty effects or learning curves might influence users' reactions, which can act as confounding variables.

Being concerned about confounding variables doesn’t mean you should stop testing—you just need to approach experiments with a critical eye and slight skepticism. Awareness of these situations helps you design stronger experiments, implement proper controls, and interpret your results more accurately.

The goal is to strike a balance between moving quickly and ensuring the reliability of your findings. When you know whether to be particularly cautious about confounding variables, you can focus your effects where they matter most, leading to more trustworthy results and better-informed decisions.

Controlling or preventing confounding variables often means addressing them before your experiment begins. You need to adjust your experimental design and use sampling processes that help account for other influences.

In real-world settings, bypassing every possible confounder is unfeasible. You should concentrate on the most likely and impactful confounding variables for your specific experiment and use these techniques to control for them.

Implementing these strategies will soon become second nature. As you get better at managing confounding variables, your tests will produce more precise, actionable insights for improving your product or website.

Experimental design best practices

Design strategies look at how you structure and carry out your test to isolate the effects of any variables—it’s like setting up your experiment to play fair.

• Use control groups: Always include a “nothing changed” control group to compare against. This baseline enables you to differentiate between the effects of your changes and other factors.
• Watch your timing: Be mindful of when you run your tests. Launching a new feature during the holidays might distort your results.
• Try crossover designs: Let participants experience the old (control) and new (treatment) product versions. This approach helps control for individual differences that might act as confounders.
• Keep things consistent: Change only what you’re testing. For instance, if you’re testing a new button color, don’t change the text size, too.
• Go blind: Sometimes, it’s best if people don’t know they’re in a test. A blind approach prevents people from altering their behavior simply because they know they're being observed
• Do A/A tests: You should occasionally run experiments where both groups see the same version of your product or website. Major differences can suggest issues in your testing setup or metrics.
• Use stats to your advantage: After your test, use techniques like regression analysis to adjust for things you couldn’t control during the experiment.

Sample selection and assignment methods

How you choose and allocate test subjects (usually current or target users) impacts the presence of confounding variables. You must ensure your sample is representative and that potential confounders are spread out.

Randomization

Many experiments begin by randomizing their test subjects to ensure a fairer distribution. This method mixes up who sees what by randomly assigning users to control and treatment groups, ensuring any confounding variables are evenly scattered.

Stratified sampling

You can also use , which divides all your users into groups based on criteria (like age, location, or device type) and randomly selects individuals from each group.

This approach provides a diverse mix of subjects for your test, ensuring the sample represents all the different groups within your user or customer base.

Blocking

Blocking is similar to stratified sampling and is often used in smaller experiments. Similar users are grouped (or blocked) and then randomly assigned a test within each block.

Blocking aims to reduce group variability so that comparisons between what you’re testing are more accurate.

Pre-screening

It’s (sometimes) okay to be picky about who’s on your test—this is called pre-screening. You might find and exclude participants with characteristics that risk confounding your results.

A good pre-screening example is omitting those with lots of prior experience with your product, as their knowledge could influence findings for . However, being overly selective can make your results less applicable to the broader population or user base.

Larger sample sizes

Aim for larger to increase the reliability of your outcome. A bigger sample size makes your results more representative and reduces the influence of outliers.

Just as asking more people for directions generally improves your chances of finding the correct path, larger sample sizes typically yield more dependable results.

Conducting trustworthy experiments is crucial but complex. helps you navigate these challenges with confidence.

The platform’s are designed with confounding variables in mind:

• Advanced : Divide your users into experiment groups to distribute confounding variables evenly.

• capabilities: Analyze your results across different user segments to spot confounding effects from behavioral or demographic factors.

• Statistical accuracy: Use built-in statistical analysis tools to help you account for confounding variables and assess the true impact of your changes.

• Real-time monitoring: Watch your experiments as they run to spot unexpected shifts that might signal the existence of confounding variables.

• Detailed reporting: Generate reports clearly communicating your findings, including any identified confounding factors.

Use Amplitude to easily identify, control, and account for confounding variables. Make with greater assurance, knowing that your experimental results truly reflect the impact of your changes.