Cognitive Biases in Data Science

Aug 2, 2018Download as PPTX, PDF1 like127 views

Biases come in many different forms, but in this article, we’ll touch on few of the major ones that are known to have considerable effects on research and science. Here is a brief list of four cognitive biases that may affect you as a researcher or data scientist.

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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
www.flydata.com

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:
www.flydata.com

#1 Confirmation
Bias
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com

Confirmation Bias
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com
● 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
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com
● 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
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com

Observation Bias
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com
● 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
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com
● 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
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com

Funding Bias
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com
● 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
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com
● 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
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com

Sampling Bias
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com
● 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
Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com
● 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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sales@flydata.com
Toll Free: 1-855-427-9787
http://flydata.com
We are an official data integration
partner of Amazon Redshift

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Cognitive Biases in Data Science

2. 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 www.flydata.com

3. 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: www.flydata.com

5. Confirmation Bias Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com ● 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!

6. Confirmation Bias Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com ● 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!

8. Observation Bias Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com ● 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!

9. Observation Bias Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com ● 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?”

11. Funding Bias Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com ● 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!

12. Example Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com ● 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.

14. Sampling Bias Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com ● 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

15. Sampling Bias Copyright ? 2014 FlyData Inc. All rights reserved. www.flydata.com ● 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!

16. www.flydata.com www.flydata.com Check us out! -> http://flydata.com sales@flydata.com Toll Free: 1-855-427-9787 http://flydata.com We are an official data integration partner of Amazon Redshift

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