We need to talk about experimental design in WordPress. For years, the standard advice for A/B testing has obsessed over Covariate Balance, and it is killing your team’s velocity. I’ve seen developers spend weeks refactoring their randomization logic because Group A ended up with more mobile users than Group B. They think a lack of balance ruins the experiment. It doesn’t.
As a senior developer, I’ve had my fair share of war stories where a client insisted we “re-roll” the randomization because the cohorts looked “off.” This is a fundamental misunderstanding of statistics. Validity isn’t about the groups being twins; it’s about the independence of the treatment assignment from any pre-existing factor.
The Myth of Necessary Covariate Balance
Randomization usually balances confounders, but it is never a guarantee. According to the Central Limit Theorem (CLT), sample means tend to follow a normal distribution. However, when you’re dealing with small sample sizes or extreme distributions, you will see imbalances. Does this undermine the experiment? No. Furthermore, if you randomly assigned the groups, the systematic relationship between the treatment and the covariates is broken.
Specifically, causal inference remains valid because any remaining association is due to chance, not selection bias. If you want to learn more about the intersection of data and dev, check out my thoughts on Data Science as Engineering.
Why Your Data Ends Up Imbalanced
- Small Sample Sizes: With fewer users, the variance is high, making large differences more likely.
- Extreme Distributions: If your user behavior is highly skewed (e.g., 1% of users generate 90% of revenue), Covariate Balance becomes harder to hit.
- Too Many Testing Groups: The more buckets you create, the higher the probability that one bucket becomes an outlier.
In practice, we use hashing to ensure a user stays in their assigned group. We don’t need a complex database lookup to “check for balance” every time a page loads. This is a common bottleneck in performance optimization.
function bbioon_get_user_experiment_group( $user_id, $experiment_id ) {
// Generate a consistent hash for the user and experiment.
$hash = md5( $user_id . $experiment_id );
// Convert first 8 chars of hash to an integer.
$val = hexdec( substr( $hash, 0, 8 ) ) % 100;
// Return 'treatment' for 50%, 'control' for the rest.
// This randomization is valid even if groups aren't perfectly balanced.
return ( $val < 50 ) ? 'treatment' : 'control';
}This approach is faster than checking a SQL table and respects the core principle of randomization. You can find more about high-performance logic in my post on WordPress Performance Optimization.
Focus on Independence, Not Just Balance
While balance is beneficial for precision, it is not a prerequisite for validity. If your randomization process is sound (using tools like random_int() in PHP), your causal inference is safe. Consequently, any “bad luck” in a single sample is just a type I or II error risk that we manage with p-values, not by manually fudging the cohorts.
“Correct randomization always breaks the systematic relationship between treatment and all covariates.” — Jarom Hulet
Look, if this Covariate Balance stuff is eating up your dev hours, let me handle it. I’ve been wrestling with WordPress since the 4.x days.
Refactor Your Mindset
Stop worrying if your groups look identical. Focus on the integrity of your randomization logic and the speed of your implementation. Use tools like PHP’s random_int for secure assignment and stop over-engineering the balance. Ship it, analyze it, and adjust for covariates in the post-test analysis if you really need to.
