Sample size calculated before experiment launch

Launching an A/B test without calculating required sample size means you have no principled stopping rule. Teams peeking at early results and declaring a winner at 200 contacts when 800 were needed inflate false positive rates from 5% to 25% or higher — a phenomenon called peeking bias. You ship subject lines, CTAs, or send times based on noise rather than signal. In iso-25010:2011 terms this is a functional-suitability failure: the experiment tooling exists but cannot reliably distinguish a real effect from random variance. The Campaign Orchestration & Sequencing Audit covers how these flawed signals propagate into branching decisions.

Implement a sample size calculator and require the field to be set before experiment launch. Block the launch action at the API layer until required_sample_size is populated:

function requiredSampleSize(
  baselineRate: number,       // e.g. 0.25 for 25% open rate
  minimumDetectableEffect: number, // e.g. 0.05 for +5pp lift
  alpha = 0.05,
  power = 0.80
): number {
  const p1 = baselineRate
  const p2 = baselineRate + minimumDetectableEffect
  const pBar = (p1 + p2) / 2
  const zAlpha = 1.96  // two-tailed, alpha=0.05
  const zBeta  = 0.842 // power=0.80
  const n = Math.pow(
    zAlpha * Math.sqrt(2 * pBar * (1 - pBar)) +
    zBeta  * Math.sqrt(p1 * (1 - p1) + p2 * (1 - p2)),
    2
  ) / Math.pow(p2 - p1, 2)
  return Math.ceil(n)
}

// Example: 25% baseline, detect +5pp lift → 768 contacts per variant
const n = requiredSampleSize(0.25, 0.05)

Store the result on the experiment record and add a pre-launch validation that rejects experiments where required_sample_size is null or zero.

• ID: campaign-analytics-attribution.ab-testing.sample-size-calculation

• Severity: high

• What to look for: Look for evidence that required sample size is computed before experiments are launched. This could appear as: a utility function that calculates required sample size given a baseline rate, minimum detectable effect, significance level, and power; documentation or configuration fields on experiment records that include required_sample_size or minimum_detectable_effect; or a pre-launch checklist or validation that requires sample size to be set. The absence of any sample size field or pre-launch calculation logic is a failure.

• Pass criteria: Experiment records or configuration include a pre-calculated required sample size. A utility or formula for computing it is present that accepts at least 3 inputs (baseline rate, minimum detectable effect, and significance level). Or an A/B testing library that handles power analysis internally (e.g., GrowthBook, Statsig) is configured with explicit minimum detectable effect settings. Count all experiment configurations and verify each includes a sample size field.

• Fail criteria: No sample size calculation found anywhere. Experiments are launched and winners declared based on whichever variant is ahead at an arbitrary time. No minimum detectable effect or statistical power settings present.

• Skip (N/A) when: The project does not run A/B tests.

• Cross-reference: The Campaign Orchestration & Sequencing Audit evaluates whether sequence branching decisions are data-driven, which depends on statistically valid experiment results.

• Detail on fail: Example: "No sample size calculation logic found — experiments appear to be evaluated at arbitrary time points" or "Experiment configuration has no required_sample_size or minimum_detectable_effect fields"

• Remediation: Calculate required sample size before launching any experiment:

  // Simple two-proportion z-test sample size calculator
  function requiredSampleSize(
    baselineRate: number,       // e.g., 0.25 for 25% open rate
    minimumDetectableEffect: number, // e.g., 0.05 for +5pp lift
    alpha: number = 0.05,       // significance level (Type I error)
    power: number = 0.80        // statistical power (1 - Type II error)
  ): number {
    const p1 = baselineRate
    const p2 = baselineRate + minimumDetectableEffect
    const pBar = (p1 + p2) / 2
  
    // z-scores for alpha/2 and beta
    const zAlpha = 1.96  // for alpha=0.05, two-tailed
    const zBeta = 0.842  // for power=0.80
  
    const numerator = Math.pow(zAlpha * Math.sqrt(2 * pBar * (1 - pBar)) + zBeta * Math.sqrt(p1 * (1 - p1) + p2 * (1 - p2)), 2)
    const denominator = Math.pow(p2 - p1, 2)
  
    return Math.ceil(numerator / denominator)
  }
  
  // Example: detect +5pp lift on 25% open rate baseline
  const n = requiredSampleSize(0.25, 0.05) // ~768 contacts per variant
  

  Store this on the experiment record and block launch until it's set.