Data analysis: Why outliers are good for science.

Updated: Oct 10, 2019


By examining the outliers we are able to ask better questions.

When #analysing data #outliers are the source of frustration. In #quality control they indicate a #problem, something has gone amiss and #deviated from the #norm. From a data perspective outliers are unwanted and mess up our vision of how we think things should and need to be. They force us to re-examine the problem and in extreme circumstances, go back to the drawing board. In all cases outliers represent instances that deviate from an expected #pattern or #outcome, nothing more. As a scientist outliers excite me and lead me to ask deeper and more intriguing #questions like #how and #why? As scientific and academic specialists outliers humble us and remind us we don’t have all the answers - reminding us of our #humanity. Not all #phenomena can be explained and forced into a box as much as we would like, as a result outliers keep us feeling vulnerable and exposed. In biology outliers are particularly exciting as they can indicate the emergence of new evolutionary traits or the decay of those no longer considered useful. #Difference and #uniqueness should be welcomed. It tells us much more than the median or the average ever could and opens up a world of #possibility. New findings that don’t fit the norm need to be investigated and not condemned. For more information on outliers check out the following link.


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