Confounding Variables

What Are Confounding Variables?

In A/B testing and online experiments, confounding variables are hidden influencers that can impact the results. They’re unaccounted-for factors that can affect your results, making it hard to tell if a change actually caused the outcome you’re seeing.

If you launch a pricing experiment and traffic happens to spike due to a marketing campaign, was the uplift due to the new price or the campaign itself? That uncertainty is what confounding variables create.

A well-run experiment attempts to isolate the variable being tested, so that everything else becomes statistical noise. But when a confounding factor is in play, that noise becomes bias.

“In a world where ‘move fast and break things’ is a leitmotif for so many online companies, understanding the true effect of initiatives can cause headaches.

For example, we run a test to understand if charging customers in their local currency boosts purchases. The test shows a positive (and significant) impact. So we decide to put it live. Unfortunately, the marketing team launched a discount campaign on the same day.

Because of the confounding variable (discount campaign), we can’t determine the true effect of the currency test.”

David Dias Rodríguez, Founder / Data Analyst, Sterling

Other Examples of Confounding Variables

Confounding variables can come from many directions. Common examples include:

• A marketing campaign launching during a test window
• An unrelated experiment running on the same page
• System bugs that impact one variant but not another
• Changes in user behavior due to seasonality
• Technical issues like flicker or slower load speeds in one variant
• A biased sample, e.g., a variant shown mostly to power users

These factors create alternative explanations for the observed outcome, undermining your confidence that the test is telling the full story.

Why Confounding Variables Matter

They make your experiment results less trustworthy.

Confounds distort your ability to measure causal impact. They lead to biased estimates, misleading conclusions, and poor decision-making. Even if your test reaches statistical significance, it doesn’t mean the result is valid if the setup was compromised.

How to Minimize Confounding Variables

You can’t eliminate every external influence, but you can control what matters:

• Randomize properly: Random assignment balances known and unknown factors between groups. It’s your first defense against bias.
• Use a control group: Without a baseline, you can’t isolate the effect of your change.
• QA your setup: Run A/A tests to catch platform bugs, SRMs, or broken tracking before running real experiments.
• Segment intelligently: Use triggered analysis to look only at users exposed to the change.
• Simplify your test: Change one variable at a time to keep causality clear.
• Monitor your data: Check for Sample Ratio Mismatches (SRMs), metric drift, or unexpected segment behavior during the test.
• Watch for overlapping experiments: Test interference is a real risk, especially on high-traffic pages.

When in doubt, consult a data scientist. It’s easier to prevent a confound than to spot one after it’s distorted your results.