The standard advice on A/B testing in WordPress has spent years fixating on Covariate Balance, and it costs teams real time. I have watched developers spend weeks rewriting randomization logic because Group A came out with more mobile users than Group B. They assumed the imbalance ruined the experiment. It did not.
I have had clients ask me to re-roll the randomization because the cohorts looked “off,” which is a misreading of what randomization actually does. Validity comes from the treatment assignment being independent of anything that existed before the test, not from the two groups looking like twins.
The myth of necessary covariate balance
Randomization usually balances confounders, but it never guarantees it. The Central Limit Theorem says sample means tend toward a normal distribution, and with small samples or extreme distributions you will still see visible imbalance. That does not undermine the experiment. Random assignment already broke the systematic relationship between the treatment and the covariates.
Causal inference holds because whatever association is left came from chance rather than selection bias. If the overlap between data work and dev work interests you, I wrote about Data Science as Engineering.
Why your data ends up imbalanced
- Small sample sizes: fewer users means higher variance, so wide gaps between groups turn up more often.
- Extreme distributions: when 1% of users generate 90% of revenue, a couple of those users landing in the same bucket skews everything, and Covariate Balance gets much harder to hit.
- Too many testing groups: every extra bucket is another chance for one of them to come out an outlier.
In practice we hash the user so they stay in whichever group they were assigned. No database lookup runs on every page load to “check for balance,” which is where this kind of thing usually turns into a performance problem.
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';
}Hashing is faster than querying a SQL table, and it respects what randomization is there for. There is more on performance-minded logic in my post on WordPress Performance Optimization.
Independence matters more than balance
Balance helps precision, but it is not a requirement for validity. If the randomization itself is sound, say with random_int() in PHP, the causal inference holds. Bad luck in a single sample is type I and type II error risk, and you handle that with p-values rather than by adjusting the cohorts by hand.
“Correct randomization always breaks the systematic relationship between treatment and all covariates.” (Jarom Hulet)
If covariate balance arguments are eating your dev hours, I can take it on. I have been working with WordPress since the 4.x days.
What to do instead
Stop checking whether the groups look identical. Put the effort into the integrity of the randomization logic and the speed of the implementation. PHP’s random_int handles secure assignment fine. Ship the test, read the results, and adjust for covariates in the analysis afterwards if it turns out you need to.
