Three biases that can influence the outcome of 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