Three biases that can influence the outcome of Data Analysis

This report will provide an insight upon three biases namely cognitive, inter-group and conformation biases that influences outcomes of data analysis, what are they and how such biases are caused. The report concludes after making suggestions on how these biases can be minimised to avoid errors in data analysis.

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Three biases that can influence the outcome of Data Analysis
Every research paper consists of significant data analysis that are designed, steered
and presented in transparent and understandable manner, without getting eccentric from the
truth. Data analysis that do not complaint with primary objectives is considered to be
misleading. Nevertheless, some analysis creates false conclusions or distorted results which
gives wrong outcomes, thereby resulting in substantial loses in businesses or other decision-
making outcomes (Friedman, 2017). This report will provide an insight upon three biases
namely cognitive, inter-group and conformation biases that influences outcomes of data
analysis, what are they and how such biases are caused. This report will conclude after
making suggestions on how these biases can be minimised to avoid errors in data analysis.
Definition of bias in data analysis
Bias is deviation from truth in data analysis, data collection and data interpretation
that creates false conclusions. Biases can take place either unintentionally or with an intention
to introduce bias in someone’s data analysis. Information providers introduce biasness in data
analysis for making their analysed data look more preferable and in favour of research
hypothesis. There are many ways through which bias decision can be implemented during
data analysis including fabricating, manipulating or abusing which results in reporting non-
existing data and inappropriate statistics in front of its readers (Montibeller & Winterfeldt,
2015, p. 1231).
Three types of biases influencing data analysis outcomes
Cognitive bias is one of those biases that follows a systematic pattern deviating from
normal and rational judgement, thereby way of thinking often resulting in illogical and
holistic influence. This mental bias can cause an individual create his/her own kind of social

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or work-related authenticity, an objective reality created from individual perception.
Moreover, the construction of reality world is never objective that results in misleading
individuals towards irrational behaviour (Montibeller & Winterfeldt, 2015). Cognitive bias
often leads to perceptual distortion, illogical interpretation and inaccurate outcomes of data
analysed. Cognitive bias can arise due to several reasons during data analysis procedure
including mental noise, information processing shortcuts, emotional motivations and limited
information gathering.
In literature, it is apparent that people do not behave rationally every time. There are
times when they are presented with more information than required thereby trying to make
quick decisions. This leads them rely on cognitive shortcuts, also known as heuristics
(Friedman, 2017). To minimise or avoid such biases, a detailed study of data analysis can be
made especially in the areas where changes have been made from the start. Change affecting
the entire decision-making process can be noted along with making out which metrics have
been changed purposely. This method can also be called as relational impact under which
reasons behind impacts over decisions can be found out to clarify data interpretation.
Rational analysis method can also be applied to make decisions after deficiency in decisions
is observed (Ehrlinger , Readinger, & Kim, 2016).
Inter-group bias is one of the biases that can take in the form of ingroup liking like
distribution of rewards among group members or protecting ingroup from negative results.
Although group standards can be evaluated on the basis of individual standards and goodness,
regardless of outgroups, most of them remains unclear thereby resulting in arousing social
comparison and misinterpretation of information’s and data. Apparently, comparison always
brings unhealthy work environment but the real issue arises when comparisons are made
unethically. Realistic conflicts theory suggests that inter-group biases arise from competitions
for resources among group members. Since each individual compete for same resource, they

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serve best interests to favour group members rather than spurning outsiders. Such kind of
behaviour may sometimes result in more hostile situations along with distorting available
data due to intense rivalry (Steffens , Reese, Ehrke, & Jonas, 2017).
Social categorization can contribute in minimising inter-group biases effectively. It
will not only motivate elimination of ethnic violence, but also make complex data simpler
through joint efforts of unbiased group activities. Secondly, the paradigmatic evidences of
cultural and ethnic violence can be equated with its outcomes and further analysed from
disciplinary perspective. Inter-group bias is not arbitrary due to which it can be controlled
easily. To avoid such biases, it is best to remain informed and aware from the beginning of
data analysis process so that thinking objectively becomes possible even though bias
threatens rationality during gathering of information’s (Steffens , Reese, Ehrke, & Jonas,
2017).
Confirmation bias takes place when individuals makes decisions according to their
own beliefs and desires. When people start assuming the idea or any data to be true, they end
up believing it and gets motivated by wishful assumptions. This leads individual stop
gathering enough information’s and knowledge and once the gathered evidences confirm so
far true, they believe it to be true. Once the data is embraced while ignoring or rejecting other
significant information, data analysis process casts several doubts in it (Calikli, Arslan, &
Berner, 2010, para. 1). The reasons behind arise of confirmation bias can be due to over
anxious individuals who interprets information to be dangerous. For instance, people with
low self-esteem can prove highly sensitive due to being ignored by other and thus constantly
shows signs for which people stop liking them. Confirmation bias may not always arise due
to data fluctuation, it may be persistent due to another particular hypothesis. Nevertheless,
potential explanation can be identified to seek evidence which supports the explanation
behind formulating such decisions (Gatlin, Hallock, & Cooley, 2017).

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To minimise conformation bias, treatment done at initial stages of information
gathering can help in making data analysis without any fluctuations and complete data set
after being reviewed for considering reasons behind data validity. Brainstorming can be done,
if possible, to identify potential causes behind unexpected fluctuations or distorting of
information. However, developing many initial hypotheses may cause development of
constraints during decision making process and therefore, using analytical procedures while
developing data analysis methods can be recommended (Luippold, Perreault, & Wainberg,
2015).
Conclusion
Complex data analysis consumes huge portion of information thereby increases problems in
effective outcomes of analysis. Such problems can not be solved easily or by simple
procedures. To remain effective, decision making must take into account every characteristic
involved in data analysis procedure along with interactions conducted to define data analysis
issues. The above report states that no matter how severe the process is, the definition of
every issue can be represented by the nature of issue. The principles described above
regarding biases influencing outcomes of analysis suggests that every data analysis and
decision-making process must contain explicit examination including challenges behind
assumptions and biases, underpinning overall data analysis process to mitigate the negative
effects of biased orientations.

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References
Calikli, G., Arslan, B., & Bener, A. (2010). PPIG, Universidad Carlos III de Madrid, 2010
www.ppig.org Confirmation Bias in Software Development and Testing: An Analysis
of the Effects of Company Size, Experience and Reasoning Skills. Retrieved from
https://www.researchgate.net/publication/235430372_Confirmation_Bias_in_Softwar
e_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Expe
rience_and_Reasoning_Skills
Ehrlinger , J., Readinger, W. O., & Kim, B. (2016). Decision-Making and Cognitive Biases.
Retrieved from https://www.researchgate.net/publication/301662722_Decision-
Making_and_Cognitive_Biases
Friedman, H. H. (2017). Cognitive Biases that Interfere with Critical Thinking and Scientific
Reasoning: A Course Module . SSRN Electronic Journal, 1-60.
Gatlin, K. P., Hallock, D., & Cooley, L. G. (2017). Confirmation Bias among Business
Students: the Impact on Decision-Making. Review of Contemporary Business
Research, 06(2), 10-15.
Luippold, B. L., Perreault, S., & Wainberg, J. (2015). 5 ways to overcome confirmation bias.
Retrieved from https://www.journalofaccountancy.com/issues/2015/feb/how-to-
overcome-confirmation-bias.html
Montibeller, G., & Winterfeldt, D. V. (2015). Cognitive and Motivational Biases in Decision
and Risk Analysis. Risk Analysis, 35(07), 1230-1251.
Steffens , M. C., Reese, G., Ehrke, F., & Jonas, K. J. (2017). When does activating diversity
alleviate, when does it increase intergroup bias? An ingroup projection perspective.

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Retrieved from
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178738