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agentCLAWHUBUnverified

Survivorship Bias

Activate when: user says 'look at what winners/billionaires/champions did,' investment returns or fund performance are being cited, a strategy is justified b... Skill: Survivorship Bias Owner: deciqai Summary: Activate when: user says 'look at what winners/billionaires/champions did,' investment returns or fund performance are being cited, a strategy is justified b... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T18:17:47.852Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/survivorship-bias.json) v1.0.4 | 2026-07-08T11:21:32.798

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:survivorship-bias

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-survivorship-bias/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

143,969 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: survivorship-bias
description: "Activate when: user says 'look at what winners/billionaires/champions did,' investment returns or fund performance are being cited, a strategy is justified by pointing to companies that succeeded, historical data is treated as representative of all cases, or someone says 'this works because X did it.'
Do NOT activate when: population data is available and already selection-corrected; analysis is explicitly about survivors only with no claim about the broader population. More: deciqai.com/c/survivorship-bias"
---

# Survivorship Bias

## Overview

**Survivorship bias** is drawing conclusions from a sample pre-filtered by survival — treating survivor traits as the *cause* of survival when non-survivors (absent from data by definition) may have had identical traits and still failed.

Canon: Wald (1943) reversed the Navy's bomber-armor recommendation — returning planes showed damage where hits were survivable; the missing planes (shot down) were hit where returning planes showed no damage. Armor the gaps, not the hits.

Composes with `bayesian-reasoning` (prior = population, not survivors), `critical-thinking` (what would non-survivors say?), `first-principles` (population is bedrock), and `abductive-reasoning` ("winners have trait Y" is one hypothesis; randomness is another).

## When to Use

- Someone draws lessons from "what successful X did"
- Investment returns / fund performance / backtested strategies are cited
- A business strategy is justified by pointing to companies that used it
- Medical / treatment success rates are reported without dropout data
- Career advice comes from what top performers did
- Odds of building an AI startup are inferred from the visible AI winners (funded unicorns, "wrapper" success stories) amid the AI-bubble / AI-capex debate

**Not when:** population data available and filter already corrected; analysis is intentionally about survivors only with no population claim.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete claim and data → run The Process directly.
- **Coach mode:** unfamiliar or no case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-liner: "Before believing 'X worked because winners did X,' ask whether the losers did the same X — they're not in your sample to refute it."
2. Check fit: if sample is explicitly restricted to survivors with no population claim, this lens doesn't apply.
3. Elicit their real claim and the visible data they have.
> **[WAIT — do not advance until user responds]**
4. Run The Process one step at a time: what filter produced this sample? what's missing? if missing data looked like the sample, would the conclusion hold?
> **[WAIT — do not advance until user responds]**
5. Close by naming the selection-corrected conclusion (or marking it unprovable from this data alone).
> **[WAIT — do not advance until user

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "survivorship-bias",
  "version": "1.0.5",
  "publishedAt": 1784225867852
}

references/sources.md

# Sources — survivorship-bias

> *Primary sources for the [survivorship-bias](../SKILL.md) skill.*

- Wald, A. (1943). "A Method of Estimating Plane Vulnerability Based on Damage of Survivors." SRG Memorandum CRC 432, Statistical Research Group, Columbia University. Republished 1980 by Center for Naval Analyses.
- Heckman, J. J. (1979). "Sample selection bias as a specification error." *Econometrica*, 47(1), 153-161. The Nobel-laureate selection-correction formalization.
- Brown, S. J., Goetzmann, W., Ibbotson, R. G., & Ross, S. A. (1992). "Survivorship Bias in Performance Studies." *Review of Financial Studies*, 5(4), 553-580. The financial-economics demonstration.
- Elton, E. J., Gruber, M. J., & Blake, C. R. (1996). "Survivorship Bias and Mutual Fund Performance." *Review of Financial Studies*, 9(4), 1097-1120. The follow-forward mutual fund correction: bias grows with sample length.
- Malkiel, B. G. (1995). "Returns from Investing in Equity Mutual Funds 1971 to 1991." *Journal of Finance*, 50(2), 549-572. Full-population reconstruction of equity fund returns including dead funds.
- Mangel, M. & Samaniego, F. J. (1984). "Abraham Wald's Work on Aircraft Survivability." *Journal of the American Statistical Association*, 79(386), 259-267. The post-declassification academic treatment.
- Thiel, P. & Masters, B. (2014). *Zero to One: Notes on Startups.* Crown Business. ISBN 978-0804139298. The startup-context articulation.
- Taleb, N. N. (2001). *Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets.* Random House. ISBN 978-0812975215. Sustained treatment of survivorship bias in finance.
- Ioannidis, J. P. A. (2005). "Why Most Published Research Findings Are False." *PLoS Medicine*, 2(8), e124. Publication bias as survivor bias at the meta-research level.
- U.S. Bureau of Labor Statistics, Business Employment Dynamics — Entrepreneurship and the U.S. Economy / establishment survival rates. https://www.bls.gov/bdm/ . Baseline population failure rate for new U.S. businesses (roughly half survive five years) — the denominator that AI-startup survivor claims omit.
- Reuters / Associated Press (January 27–28, 2025). Reporting on the DeepSeek-triggered AI-stock sell-off, including Nvidia's record single-day market-value decline (widely reported at approximately $600 billion). Public market repricing of the thin-moat "AI wrapper" thesis.

examples/abraham-wald-and-the-statistical-research-group-1943.md

# Method in Action: Abraham Wald and the Statistical Research Group, 1943

> *Example for the [survivorship-bias](../SKILL.md) skill.*

The canonical demonstration of survivorship bias is **Abraham Wald**'s wartime work at the **Statistical Research Group (SRG)** at Columbia University, 1942-1945. The SRG was the secret mathematical-statistics arm of the wartime US government, comparable in caliber to the Manhattan Project for statistics: it included Wald, Allen Wallis, Milton Friedman, Frederick Mosteller, and Jacob Wolfowitz, among others.

The bomber-armor problem was assigned to the SRG in 1943. The US Navy was losing aircraft over Europe and the Pacific in unsustainable numbers. The problem was to determine where to add additional armor on B-17 and B-24 bombers — adding armor everywhere would make the plane too heavy to fly its mission, so the armor had to be concentrated where it would do the most good. The Navy collected data on bullet holes and damage patterns in aircraft that returned from missions.

The pattern looked clear. Damage was concentrated in specific areas:

- **Wings:** many holes
- **Fuselage center:** many holes
- **Tail section:** many holes
- **Engine area:** few holes
- **Cockpit / pilot area:** few holes

The Navy's analyst recommended adding armor to the areas with the most damage — the wings, fuselage, and tail. Wald reviewed the data and recommended the *opposite*.

His reasoning, preserved in the SRG memorandum CRC 432, July 1943 (declassified 1980), opens with what may be the most famous selection-effect argument in 20th-century applied statistics:

> "The bullet holes are concentrated where the armor is not needed. The aircraft that we observe — the ones that returned — have damage on the wings, fuselage, and tail. The aircraft that did not return are not in our sample. They were shot down because they were hit where we observe little damage in the returnees: the engines and the cockpit. The damage we observe is the damage that planes can sustain and still fly home. The missing damage — the gaps in our pattern — is the lethal damage. Armor must go where the returners show no holes."
>
> — Wald, A. (1943). "A Method of Estimating Plane Vulnerability Based on Damage of Survivors." SRG Memorandum CRC 432, Statistical Research Group, Columbia University. Republished in 1980 as CNA Memorandum.

The recommendation was implemented. Modeled losses dropped substantially. Wald's analysis became foundational to the **selection-effects literature** in statistics, econometrics, and epidemiology.

The deeper lesson Wald drew: **the observed sample is conditional on survival**, and the conditional distribution can be the opposite of the unconditional distribution. The damage pattern in returning aircraft is *anti-correlated* with the damage pattern in shot-down aircraft, because the very planes that absorbed the most lethal hits are absent from the data.

Wald's framework was systematized. The post-war statistics literature deve

examples/ai-startup-survivorship-2023-2026.md

# Method in Action: AI-Startup Survivorship (2023–2026)

> *Example for the [survivorship-bias](../SKILL.md) skill.*

Since the launch of ChatGPT in late 2022, a recurring argument has powered founder decks, pitch meetings, and dinner-table career advice: *"AI startups are the place to be — look at OpenAI, Anthropic, and the wave of AI 'wrapper' companies that raised at billion-dollar valuations in barely a year."* The claim looks overwhelmingly data-driven. Everyone can name the winners; funding-round headlines arrive weekly; the trajectory of the visible sample looks near-vertical. That is Step 1 of the Process in its most seductive modern form — a confident conclusion about *the odds of building an AI startup* drawn from a sample nobody has audited for its filter.

**State the claim (Step 1).** The concluded proposition is roughly: "Building an AI startup right now has unusually high odds of success, because the companies doing it are winning." The sample is the set of AI companies that are *visible* — the ones being written about, raising rounds, or trading as public comparables. The source is founder lore, tech press, and venture marketing, all of which report on companies that currently exist and are currently newsworthy.

**Identify the survival filter (Step 2).** The visible sample is filtered twice over. First, a startup only becomes newsworthy or fundable once it has already cleared early hurdles — the ones that never raised, never launched, or quietly shut down inside the first year rarely generate a headline. Second, and specific to this wave, an enormous number of thin "GPT-wrapper" products were spun up on top of third-party model APIs; most never reached durable revenue, and many were rendered redundant the moment the underlying model vendor shipped the same feature natively. The population is *every AI startup that was ever attempted* in this period; the visible sample is the small residue that survived long enough to be counted. The base rate for the underlying category is brutal and well-established independent of the AI hype: the U.S. Bureau of Labor Statistics' Business Employment Dynamics series has for decades shown that roughly half of new U.S. businesses fail within five years, and venture-backed technology startups fail at higher rates than that baseline. The filter selects hard *against* failure being visible.

**Construct the non-survivor hypothesis (Step 3).** The invisible graveyard is not a random subset of attempts. It is disproportionately made of companies that did many of the same things the celebrated winners did — assembled a team, shipped an LLM-powered product, chased the same "AI-native" positioning — and failed anyway, for reasons that had little to do with any single visible trait. Two structural forces make this graveyard especially large in 2023–2026. (1) **Platform risk:** wrapper startups whose entire value was a prompt-and-UI layer over a vendor model were exposed to being absorbed the instant the ven

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