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.