Cognitive Biases in Data Science

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Cognitive Biases in
Data Science
Copyright © 2014 FlyData Inc. All rights reserved. www.flydata.com

Introduction
Copyright © 2014 FlyData Inc. All rights reserved.
● We often think of “data” as objective information
● In reality, data can be just as subjective as the
people who record it!
● In scientific fields especially…
○ empirical methods are used to observe nature
○ data should always be collected and
interpreted impartially
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Introduction
Copyright © 2014 FlyData Inc. All rights reserved.
● Cognitive biases are an obstacle when trying to
interpret information
○ Can easily skew results
○ They are innate tendencies
● Here are 4 major biases that are known to have
considerable effects on research and science:
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#1 Confirmation
Bias

Confirmation Bias
● Confirmation bias is the tendency to process
information in a way that confirms one’s
preconceptions or hypotheses.
○ Actively seek out and assign more value to
data that confirms our own hypotheses...
○ And ignore/understate evidence that could
mean otherwise!

Confirmation Bias
● You may have “good” preconceptions from an
educated intuition or previous experiences…
● But it’s not like that in many cases!
○ Can directly affect the results of a study
or analysis!

#2 Observation
Bias

Observation Bias
● The tendency to look in places where it is
expected to produce good results, or where it is
very convenient to observe
○ Easy accessibility/availability doesn’t mean
it’s the most important!
● The most available and known data source
may often be a good one…
○ But no data analysis is complete without a
complete picture of your data.
● Data science is about producing actionable
insights
○ If only the wrong things are being observed
and measured, you produce false insights!

Observation Bias
● To be an efficient researcher, perhaps it’s
best to frequently ask yourself these
questions:
○ “Am I measuring the right things?”
○ “Are there better sources from which to
get data from?”

#3 Funding Bias

Funding Bias
● Unconscious tendency to skew models, data,
or interpretations of data in a way that favors
the objectives of a financial sponsor or
employer.
○ Sometimes called sponsorship bias
● Any scientist/researcher should keep this in
mind
○ Unknowingly making a business decision
with flawed data will ultimately damage
sponsor!
○ Will damage your career
○ ..and it’s just bad science!

Example
● In the 1990’s, the tobacco industry funded a
number of research studies on the effects of
tobacco and smoking cigarettes
● After investigation, industry sponsors and
research centers were found to
○ Present findings in a misleading way
○ Withhold certain findings about the
relationships between smoking and
cancer
● This is a prime example of a funding bias.

#4 Sampling Bias

Sampling Bias
● In experimentation, we take a sample, which
should be representative of a whole population
○ Achieved by statistical techniques and well-
designed randomization
○ What happens if proper randomization isn’t
achieved?
● It’s not uncommon for researchers to have a
sampling bias
○ Selection of groups or data for
experimentation is unintentionally not
representative of the population

Sampling Bias
● No matter how big/diverse the sample is..
○ Always a possibility of inconsistency in
data/sample collection
● This bias also ties in with the other 3 biases!
○ If any of those biases affects the way in
which you collect samples, then you’re
also experiencing a sampling bias!

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Cognitive Biases in Data Science

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