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A false negative in A/B testing is a test result that incorrectly indicates no significant difference between control and variant when a genuine improvement actually exists. You conclude the test has "no winner" and revert to control — but the variant was truly better, and you've missed a real conversion lift. False negatives are synonymous with Type II errors. The probability of a false negative is beta (β); reducing it requires higher statistical power (1 − β), which in practice means larger sample sizes.
Key relationship: False Negative Rate = β = 1 − Statistical Power. At 80% power, 20% of tests with real effects will produce false negatives.
False negatives are the silent killer of A/B testing programmes. Unlike false positives (which eventually reveal themselves when shipped variants fail to perform), false negatives leave no trace — you simply don't know you missed a real improvement. Over time, a team plagued by false negatives will progressively underestimate the impact of CRO, conclude that "testing doesn't work for our site," and reduce their investment in experimentation.
For Indian D2C brands in categories with thin margins (fashion, FMCG, supplements), even a 3–5% lift in checkout completion rate or repeat purchase rate can represent significant annual revenue. A false negative on a test that would have delivered a 4% lift means that lift is permanently forgone — or rediscovered only months later when a competitor implements the same change.
False negatives are systematically more common than teams realise because the most frequent cause — insufficient sample size — is invisible at the time. The test looks fine; it just ends "flat" and is closed without anyone realising the sample was never sufficient to detect the effect.
A Pune-based D2C pet food brand tests a subscription offer on their product page: a "subscribe & save 15%" badge vs. no badge. Based on industry benchmarks, they expect a 6% relative lift in subscription sign-ups. Their subscription page gets 600 visitors/day. A power analysis (had they run one) would show they need 14,800 visitors per variant — about 49 days at 50/50 split. Instead, they run the test for 10 days (6,000 per variant) and see p = 0.18. They conclude the badge doesn't work and remove it. Their power at 6,000 per variant is approximately 35% — they had a 65% chance of a false negative. The badge may well have worked; the test was simply far too small to know. A proper-duration test would have settled the question. The subscription revenue they missed is untracked and unmissed.
False negatives represent missed revenue — improvements that existed but were never detected. The primary lever for reducing false negative rate is statistical power, which is controlled through sample size. Building a culture that takes power analysis seriously, and that doesn't close tests prematurely, is the most direct path to reducing false negatives in an ecommerce CRO programme.
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