Revisiting Randomized Experiments: A Comparison Point
- Maria Alice Maia
- Mar 17
- 3 min read
Everyone loves to call Randomized Controlled Trials (RCTs) the “gold standard” for measuring impact. But is the gold standard always the right standard?
Let’s get one thing straight: the core idea of an RCT is brutally effective. You want to know if your new sales strategy actually works? Randomly assign half your team to use it and half to stick with the old way. Because the only systematic difference between the two groups is the new strategy, any difference in their results is its causal effect.
It’s clean. It’s powerful. It’s the closest we get to scientific truth in the messy world of business. It cuts through the noise and the excuses.
And yet, most companies don’t do it.
The "Doing Data Wrong" Scenario: The Fear of Knowing
The most common—and costly—data mistake I see isn't a bad analysis; it's the absence of analysis. It's the paralysis that comes from thinking experimentation is too hard, too slow, or too expensive.
A business will spend millions launching a new product nationwide based on gut feelings and a few surveys. But they won’t spend a fraction of that to run a small, randomized pilot in a few cities to prove it works first.
This isn’t just "doing data wrong." It's a failure of nerve. It’s choosing to guess when you have the power to know.
The Power of a Real Experiment
Imagine you're in HR or leading a Sales Department. You've developed an expensive new leadership training program.
The Wrong Way (Guesswork): You roll it out to all your managers. Six months later, employee retention is up 5%. Was it your program? Or the new company-wide bonus structure? Or just a better economy? You will neverknow. You can't justify the ROI, and next year, your budget is cut.
The Right Way (RCT): You randomly select 50 managers for the new training (the "treatment group"). Another 50 get the existing training (the "control group"). You ensure the groups are comparable. Six months later, you compare their teams' retention rates. The difference is the causal impact of your investment. Now you have an undeniable result to show the board.
So, Why Isn't Everyone Doing This?
RCTs are the gold standard, but they aren’t a silver bullet. They have limitations: they can be expensive, they raise ethical questions if the treatment could be harmful, and sometimes they're just not feasible. You can't randomly assign half the country to a new tax policy.
And that’s okay. When a true RCT is off the table, we have an incredible toolkit of quasi-experimental methods (like the advanced Difference-in-Differences models developed by brilliant minds like Callaway & Sant'Anna) that get us damn close to the truth.
The key is to not let the pursuit of perfection stop you from getting started.
Managers: Your job is to de-risk big decisions. For your next major initiative, don't just ask for a forecast. Ask, "How can we run a simple, small-scale experiment to prove this concept before we bet the farm?" Challenge your teams to move from correlation to causation.
Tech & Data Professionals: Be the champion for a culture of experimentation. You have the skills to design and execute these tests. Proactively identify the company's most critical assumptions and propose a pilot RCT to validate them. This is how you transform your function from a cost center to the engine of smart growth.
From my time at Ambev and Itaú to building my own company, the biggest wins came from having the courage to test our assumptions with rigor. This knowledge is fundamental, and it’s not mine to hoard.
If you’re ready to move from guessing to knowing, from PowerPoints to proof, join my private email list. It’s a community for leaders who demand real answers. Let's build it together.
