Verifying Random A/B Testing Groups

To verify that assignment to A/B testing groups was truly random, start by comparing key user characteristics between the treatment and control groups using statistical tests.

For continuous variables like session duration or time spent, t-tests or non-parametric alternatives like the Mann-Whitney U test can assess whether the distributions significantly differ. For categorical variables such as device type, user region, or account type, use chi-square tests or Fisher’s exact test.

In both cases, if the p-values are above a standard significance threshold (e.g., p > 0.05), it indicates there are no significant differences, suggesting the groups are balanced and the assignment was likely random.

As an additional diagnostic tool, you can perform propensity score modeling by fitting a logistic regression model that predicts the probability of treatment assignment based on observable user attributes. If the model performs poorly (e.g., low AUC near 0.5), this implies that the observed variables do not predict group assignment well, further supporting the case for proper randomization. This step is crucial because unbalanced groups can introduce bias and invalidate the causal conclusions of an experiment.