Ensuring Accurate Traffic Splits in A/B Testing

Last updated: June 22, 2026

Problem Statement

Teams sometimes observe uneven traffic splits in Uniform A/B tests — overall, or when results are broken down by dimensions such as device type or entry page. This can raise concerns that high-converting users are overrepresented in one variant and skewing results.

Solution

1. Understand how Uniform assigns variants

Uniform's randomization targets a 50/50 split at scale:

  1. A visitor lands on a page with an active A/B test.

  2. The algorithm generates a random number between 1 and 100: 1–50 → Group A (control), 51–100 → Group B (variant).

  3. The assignment is stored in local storage, so the visitor keeps the same variant across visits.

Over large datasets (100,000+ visitors) deviations average out. Small datasets and granular breakdowns are subject to natural variance.

2. Know why granular splits look uneven

  • Natural variance in small samples: below ~1,000 visitors, random sampling alone causes visible deviation from 50/50.

  • Non-random user behavior: dimensions like device type or entry page are not randomly distributed — e.g. iPhone users may favor certain entry points — so segment-level splits can look skewed even when randomization is correct.

3. Validate splits statistically

Use a Chi-Square test to check whether an observed deviation is statistically significant:

function chiSquareTest(count, observedBelow50, observedAbove50) {
  const expectedBelow50 = count * 0.5;
  const expectedAbove50 = count * 0.5;
  const chiSquare =
    ((observedBelow50 - expectedBelow50) ** 2) / expectedBelow50 +
    ((observedAbove50 - expectedAbove50) ** 2) / expectedAbove50;
  const criticalValue = 3.84; // χ² critical value at p = 0.05, df = 1
  const isSignificant = chiSquare > criticalValue;
  console.log("Calculated χ² value:", chiSquare.toFixed(2));
  console.log(`The deviation is ${isSignificant ? "significant" : "not significant"} at the 0.05 level.`);
}

chiSquareTest(100, 53, 47);

A χ² value below 3.84 means the deviation is within normal randomness — no action needed.

4. Apply standard practices

  • Segment when control matters: create separate A/B tests for distinct audience segments (device type, entry page) when accurate per-segment splits are required.

  • Monitor over time: review distributions as the dataset grows.

  • Analyze aggregates: rely on overall results rather than small subsets prone to variance.

Troubleshooting

Verify it works: after meaningful traffic (ideally 1,000+ visitors), confirm the overall assignment is within a few percent of the configured split and the Chi-Square test reports the deviation as not significant.

iPhone or entry-page skew: caused by non-random user behavior, not the algorithm — run a separate test segmented by that dimension.

Assignments change between visits: local storage was cleared or the visitor switched devices/browsers; this doesn't invalidate the test — rely on aggregates.

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