B
Evidence Grade B
Graded
accuracy verified by the review judge
Safe to Cite
Recent
26%
26% -- sample size reduction for Sequential A/B testing (2025)
Sequential A/B testing reduces required sample size by approximately 26%.
- Increases the chance of missing a real winner by 123%
Attribution:
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Lighthouse Research Team
Last verified:
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Source verified
Evidence graded
Value
26%
Unit
Percentage
Data Vintage
2025
Entity
Evidence Quality
B
Strong, reliable evidence
Sourced from a well-regarded research firm or industry leader with transparent methodology and a reasonably large sample.
Single source - not yet corroborated
Score breakdown
Extraction
75
Source Trust
60
Citation Chain
20
Independence
30
Methodology
45
Freshness
95
Source
kirro.io
Primary
Tier B
https://kirro.io/ab-test-sample-size-formula ↗
Source Article
A/B Test Sample Size Formula, Explained With Examples
The A/B test sample size formula is:
n = (Zα/2 + Zβ)² × [p₁(1−p₁) + p₂(1−p₂)] / (p₁−p₂)²
If that looks like gibberish, you’re not alone. Most articles either throw this formula at you with zero explanation, or skip it and say “just use a calculator.”
This guide does both. We’ll break down every piec…
Original Research
kirro.io
· Original study ↗
Methodology
Tradeoff of sequential testing vs fixed-horizon testing
Methodology documented
Segmentation
Industrymarketing
Cite this stat
Plain Text
Sequential A/B testing: 26% sample size reduction. Source: kirro.io (2025). Via Lighthouse Intelligence -- https://lighthousedata.io/data/sequential-testing-sample-reduction-26pct
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<blockquote cite="https://lighthousedata.io/data/sequential-testing-sample-reduction-26pct" style="border-left:3px solid #2563eb;padding:12px 16px;margin:16px 0;font-family:system-ui,sans-serif;background:#f0f4ff;"><strong>26%</strong> -- sample size reduction for Sequential A/B testing<br><small>Source: kirro.io (2025) · <a href="https://lighthousedata.io/data/sequential-testing-sample-reduction-26pct" target="_blank" rel="noopener">Lighthouse Intelligence</a></small></blockquote>
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