Beyond Simple Metrics: Why Your A/B Testing Needs More Than Just T-test Results

When running A/B tests, most teams stop at the surface-level question: “Did the metric move?” But what if we told you there’s a smarter way to extract deeper insights from your experimental data? Let’s explore why linear regression deserves a seat at your analytics table, even when T-test seems sufficient.

The Classic Approach: T-test on Session Data

Imagine an e-commerce platform launches a redesigned banner and wants to measure its impact on user session length. The straightforward path? Deploy a T-test.

Running the numbers gives us a treatment effect of 0.56 minutes—meaning users spend roughly 33 seconds longer in sessions. This uplift is calculated as the simple difference between control and treatment group averages. Clean, easy to explain, job done, right?

Not quite.

The Linear Regression Alternative: Same Answer, Different Depth

Now let’s frame the exact same experiment through linear regression. We set treatment status (banner shown: yes/no) as our independent variable and session length as our dependent variable.

Here’s where it gets interesting: the regression coefficient for treatment comes out to 0.56—identical to the T-test result.

This isn’t coincidence. Both methods are testing the same null hypothesis. When you run a T-test, you’re asking: “Is there a significant difference in means?” Linear regression asks: “Does the treatment variable explain the variance in session length?” With a single binary treatment variable, these questions collapse into the same mathematical problem.

But look at the R-squared value: just 0.008. The model explains almost nothing about what drives session length variation. This limitation hints at a critical flaw in our analysis.

The Hidden Problem: Selection Bias in Your Experiment

Here’s the uncomfortable truth: random assignment in A/B tests doesn’t eliminate selection bias—it only reduces it.

Selection bias occurs when systematic differences between your control and treatment groups exist beyond the treatment itself. For example:

• Returning users encounter the banner more frequently than new visitors
• Time-of-day effects correlate with treatment exposure
• User segments experience the banner differently

In such cases, your 0.56-minute uplift might be inflated or deflated by these confounding factors. You’re measuring a blended effect: true treatment impact plus selection bias.

The Solution: Add Context with Covariates

This is where linear regression shines. By incorporating confounding variables (covariates), you isolate the true treatment effect from background noise.

Let’s add pre-experiment session length as a covariate—essentially asking: “Given that users had baseline session patterns, how much did the banner truly change their behavior?”

The results transform dramatically. R-squared jumps to 0.86, meaning 86% of variance is now explained. And the treatment coefficient drops to 0.47.

Which number is right—0.56 or 0.47? When we simulate the ground truth with a known 0.5-minute uplift baked in, 0.47 is demonstrably closer. The covariate-adjusted model wins.

Why This Matters for Your Decisions

1. Model fit improves, revealing whether your experimental design is capturing the real drivers of user behavior
2. Bias correction happens automatically, reducing the risk of making decisions on inflated/deflated effect sizes
3. Confidence increases, because you’re no longer vulnerable to hidden confounders distorting your results

Beyond T-test and Linear Regression

The principle extends further. Your statistical toolkit includes other tests—Chi-square test in R, Welch’s t-test, and more specialized approaches. Each can be reframed through regression with appropriate model adjustments.

The takeaway: next time you’re tempted to trust a single statistical test, ask whether lurking variables might be distorting your picture. Linear regression with thoughtfully selected covariates transforms A/B testing from a binary pass/fail check into a nuanced causal investigation.

Your metrics will thank you.