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Cognitive Biases: Survivorship Bias

Updated: Jan 7


Definition:

A type of faulty generalization wherby a conclusion is reached on weak premises.


Survivorship bias is a form of selection bias occurring when a successful subgroup of people or things are mistaken as an entire group, usually due to the failure subgroup not being visible.


We only see the 'survivors' leading to false conclusions.


Examples:


The media only reports on the small number of underdog millionaires who won big despite adversity. We don't hear about the thousands of people with similar skills that worked diligently but never achieved success. This leads to the false perception that anyone can make it if they work hard enough.

Every person alive today has various survivorship biases.


We are also currently living through a selection process in the form of a viral pandemic.


Those who don't survive the virus are not visible to all of us and aren't around to report on it's severity, we only hear from survivors, which may infer false perceptions.


Those who die without being tested for the virus cannot be considered part of the death count, potentially skewing survival rates.


Many nations and health systems cannot keep up with testing, resulting in potential survivorship bias when looking at the data generated from the disease.


Publication bias is a form of survivorship bias where journals prefer to publish positive findings.


As the body of published data on a topic is missing negative or null findings it can lead to false conclusions being drawn about the efficacy of a treatment, and iatrogenic harm to those who go on to receive it.


Almost all studies that recruit participants experience a number of 'drop outs' as the trial goes on resulting in full data being collected only from survivors.


This potentially selects for only participants that were committed to the program and had strong buy-in about the intervention (whether assigned to a placebo group or not) resulting in some survivorship bias in the findings.


When reading studies it's worth paying attention to attrition rates.


Think about:

  • diet plans

  • exercise programs

  • rehab treatments

  • health products


that you've seen glowing testimonials about.


It's likely that only the people that saw results (real or placebo) will share reviews of these.


We often feel failure or shame if we didn't see results so choose not to share that information with others, skewing the sample.


Survivorship Bias: Ask yourself, what am I not seeing?

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