We’ve all seen it: the team launches a shiny new feature, the numbers spike, and everyone celebrates. But no one asks the most important question—compared to what? Without a control group, that spike might be nothing more than a coincidence or the result of external noise.

One marketing team I worked with launched a redesigned email template and saw click-throughs jump 18%. They declared success—until we looked closer. There had been no control group. When we finally ran a structured A/B test, the “lift” disappeared. It turned out the spike had come from a concurrent promo campaign sent the same week. The template had done nothing.

Without a control group, we weren’t testing. We were guessing.

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The Forgotten Control Group

A control group is a baseline group that doesn’t receive the experimental treatment. It’s the yardstick for comparison—the “business as usual” against which we evaluate the effect of our changes. When used correctly, control groups isolate the true impact of a test variable. When ignored, they leave us open to misleading conclusions.

Despite this, control groups are often an afterthought—or worse, deliberately omitted to “just get results.” Sometimes teams believe they already know the outcome, or they fear losing short-term gains by holding back a portion of the audience. Others assume that comparing this month’s results to last month’s is “close enough.” It’s not.

Control groups are not optional. They are what make an experiment… an experiment.

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Common Control Group Missteps

If you’ve ever worked on a rushed experiment, you’ve probably seen one (or more) of these common control pitfalls:

1. No Control Group at All

This is the most obvious and dangerous error. You roll out a change to 100% of users and observe a result—but you have no baseline for comparison. Was the result driven by your change or by something seasonal? Did the market shift? Was there a bug fix elsewhere that influenced behavior? You’ll never know.

2. Contaminated or Biased Controls

Sometimes a team tries to include a control group, but the group isn’t truly isolated. Perhaps the “control” users were exposed to some aspects of the treatment indirectly (e.g., cross-channel bleed), or the control group was selected in a biased way (e.g., choosing low-performing users as the control to make the test look good). A contaminated control invalidates the comparison.

3. Moving the Goalposts Mid-Test

Changing the definition of the control group—or redefining what qualifies as “business as usual”—in the middle of a test is another common blunder. Sometimes it’s unintentional: a new campaign launches halfway through the test that affects only the control group. Other times it’s deliberate: redefining control to better match the desired narrative. Either way, it ruins the validity of the experiment.

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The Cost of Bad Controls

When control groups are mishandled or ignored, the damage ripples beyond just one test.

You may reach invalid conclusions that mislead future strategies. A feature rolled out under the false belief that it works may, in reality, be neutral—or even harmful. This can compound over time, especially when decision-makers trust those results to drive product or marketing roadmaps.

You also waste time and resources. Running experiments isn’t free—it takes planning, implementation, monitoring, and analysis. If your test design is flawed from the beginning, you’ve just burned cycles on something that can’t be trusted.

Perhaps most dangerously, poor controls foster false confidence. Teams feel like they’re being data-driven. Dashboards are updated. PowerPoints are made. Leadership is impressed. But if the conclusions rest on faulty comparisons, the organization is building on sand.

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Best Practices for Control Group Design

Let’s talk about how to get it right.

1. Design Fair Comparisons from the Start

Your control group should be randomly selected from the same population as your treatment group and should differ only in that they didn’t receive the change. This creates a fair apples-to-apples comparison. Don’t cherry-pick. Don’t make excuses. Randomization is your friend.

2. Keep the Control Group Stable

Control groups must remain consistent throughout the test. This means not exposing them to parts of the treatment or letting them leak into other audiences. In digital environments, this may require engineering effort to properly segment and isolate users.

3. Monitor Your Controls Over Time

Don’t just check at the end of the test. Make sure control behavior is behaving as expected over time. If you see big swings in the control group, investigate. Something else may be going on—seasonality, platform bugs, or outside influence—that could mask or mimic the treatment effect.

Bonus tip: in high-volume or long-term experiments, consider stratifying your randomization or using pre-test matching techniques to improve statistical power.

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Controls Are Non-Negotiable

If you take away one thing from this post, let it be this: control groups are not a luxury. They are a necessity.

They don’t slow down your testing process—they protect it. They don’t waste conversions—they help you avoid costly missteps. Without them, you’re not experimenting. You’re making educated guesses at best, and risky assumptions at worst.

In a business climate that increasingly prizes agility, data-driven insights, and ROI, the rigor of your testing process is your competitive advantage. And that starts with well-designed controls.

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Call to Action

Before you run your next experiment—or use data to defend the last one—ask yourself:

• Was there a proper control group?
• Was it randomized and unbiased?
• Did it remain isolated throughout the test?
• Could external factors have influenced the comparison?

Better yet, pull up the last test you ran and audit it. If you find signs of contamination, shifting baselines, or control neglect—use it as a learning moment. Share your insights. Improve the next one.

And if you’ve got war stories about control group disasters (we all do), I’d love to hear them. Comment below or message me directly. Let’s build a better experimentation culture—one well-designed test at a time.