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Tuesday, April 18, 2017

How to Mislead with Data

 I have seen it often in the enterprise and elsewhere.  Good points made in article below, and always worth repeating.    But missing is the current tendency to dress the results. Aka 'Tell a Story', or use some sort of pre-established narrative.  Or use an infographic to simplify into an instantly understandable point to be made.    Yes, that's confirmation bias, or model-confirmation bias.

Also very common and hard to detect unless you are shown the raw data and can manipulate it, which is rare.  Helps to do a risk analysis to understand the cost of a wrong model. Or involve the context owner early and often.

How to Lie with Data  Posted by Karolis Urbonas    In DSC

We expect that data scientists and analysts should be objective and base their conclusions on data. Now while the name of the job implies that “data” is the fundamental material that is used to do their jobs, it is not impossible to lie with it. Quite the opposite – the data scientist is affected by unconscious biases, peer pressure, urgency, and if that’s not enough – there are inherent risks in the process of data analysis and interpretation that lead to lying. It happens all the time while the intentions might be truly honest – though we all know the saying “The road to Hell is paved with good intentions”.

As every industry in every country is affected by data revolution we need to make sure we are aware of the dangerous mechanisms that can affect the output of any data project..... " 

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