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Plate BIAS·010 · Cognitive biasGrid A2 · Rev. 2026-08-14

Survivorship Bias

Analysing only the people who made it through hides everything the failures would have told you.

Cognitive biasEstablished researchIntermediate

Definition

A selection bias in which conclusions are drawn from a sample that has already passed a filter, ignoring the cases that did not. The classic illustration is Abraham Wald's wartime analysis of aircraft damage, where the returning planes showed where damage was survivable.

Established research. Originates in published research and has been replicated. The effect is real. Its size in your product still needs measuring.

On this plate
  1. 1Definition
  2. 2How it shows up in product data
  3. 3Who is missing from each source
  4. 4In practice
  5. 5Local chart
  6. 6Sources
  7. 7Continue from here

Reach for this when

  • Interpreting any analytics or survey result.
  • Planning recruitment for research.
  • Reviewing a conclusion drawn from successful cases.
  • A decision rests on data drawn only from people currently using the product.

Common mistakes

  • Recruiting only from active users.
  • Confusing completion rate with usability.
  • Ignoring the top of the funnel.

How it shows up in product data

  • Surveying only active users, who by definition did not churn.
  • Analytics on completed funnels, which cannot see people who never entered.
  • NPS from a subscription base that has already excluded everyone who left.
  • Usability testing with recruited enthusiasts.
  • Reading five-star reviews to understand why people buy.
  • Post-launch analysis that only includes customers who upgraded.

Do

  • Interview people who churned, and people who evaluated and chose a competitor.
  • Instrument the top of the funnel, including entries that never progress.
  • Segment analysis by cohort so the survivors are visible as a subset.
  • Ask what the missing population would have said, explicitly, in every readout.

Do not

  • Generalise from your most engaged users.
  • Treat completion rates as a measure of usability without knowing who dropped out.
  • Assume an unused feature is unwanted rather than undiscovered.

Who is missing from each source

Every data source in a product surveys the people who stayed. Naming the absent group is the whole discipline.

SourceWho it cannot see
Product analyticsPeople who never signed up, and those who left
In-app surveyAnyone who stopped opening the app
Support ticketsPeople who gave up instead of asking
ReviewsThe indifferent majority in the middle
Power user interviewsEveryone for whom the product did not work
Funnel dataThose who never entered the funnel

In practice

  1. i.

    Churn interviews

    Subscription products

    The most valuable research population is the one that left. They are harder to recruit and they answer questions your active users cannot.

  2. ii.

    Funnel analysis blind spots

    Analytics

    A checkout with a ninety percent completion rate looks healthy until you measure how many people reached checkout at all, and why the rest did not.

  3. iii.

    The feature loved by the people still using it

    Reading retained users

    Satisfaction with a complex workflow was high among current users, because the people it defeated had already left. The number was accurate and the conclusion drawn from it was the opposite of the truth.

Local chart

BIAS·010 · Region I · Grid A2

This entry and the entries it is stated to connect with. Each line is labelled with what the connection means.

A/B TestingRES·009 · APPLIED THROUGHFunnel AnalysisRES·011 · APPLIED THROUGHGuerrilla TestingRES·016 · APPLIED THROUGHProduct AnalyticsRES·010 · APPLIED THROUGHSurveysRES·004 · APPLIED THROUGHUser InterviewsRES·003 · APPLIED THROUGHMeasuring UXFLOW·011 · APPLIED THROUGHPersonaTPL·003 · OFTEN USED WITHResearch PlanTPL·001 · OFTEN USED WITHAmplitudeTOOL·023 · OFTEN USED WITHUser InterviewsTOOL·016 · OFTEN USED WITHChurnTERM·102 · SHORT DEFINITION

Sources

Where a source establishes something narrower than the popular reading of it, the note says so.

  1. [1]

    A method of estimating plane vulnerability based on damage of survivors

    A. Wald, Statistical Research Group, Columbia University, 1943

    The canonical case: armour belongs where the returning aircraft were not hit, because those hits were on the planes that did not return.

Continue from here

Each link says what the connection is, so you can tell a principle from an alternative from a thing people mix this up with.

Applied through

Where this shows up as a concrete interface decision.

Often used with

These usually appear in the same screen or the same decision.

Short definition

The one-paragraph version, for when that is all you need.

Related concept

Connected closely enough to change how you apply this.