9 Causes Of Data Misinterpretation - InformationWeek

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7/17/2015
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Lisa Morgan
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9 Causes Of Data Misinterpretation

Data can prove just about anything. Most organizations want to come to the right decisions, but faulty conclusions and bad outcomes can happen. Here's why.
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Important Variables Are Omitted 

A single missing variable can cause data to be misinterpreted. And when data is misinterpreted, it leads to faulty conclusions and sometimes unwise investments.
'This is the 'minefield' of using data,' said StorageMart chief marketing officer Tron Jordheim in an interview. 'There are so many obvious variables and an unknown quantity of unknown or obscure variables.' Even if you have been prudent about identifying variables, Jordheim said, 'after you make the decision you have to know that you may [nevertheless] have missed a variable. You will be on the lookout for curious or odd results after the decision that indicate you missed a variable.'
When retail promotions are more (or less) successful than expected, it may be because important variables are missing. 
'In low-income areas, there tends to be more shopping at the first of the month, so stores may do promotions to fill in the valleys,' said Ken Gilbert, professor emeritus at the University of Tennessee. 'If you look at the relationship between promotions and sales, it would look like promotions hurt sales. To find out the real effect, you have to do [tests] where you experiment in high-demand or low-demand periods, or you control for the seasonality in demands.' 
(Image: Jodylehigh via Pixabay)

Important Variables Are Omitted

A single missing variable can cause data to be misinterpreted. And when data is misinterpreted, it leads to faulty conclusions and sometimes unwise investments.

"This is the 'minefield' of using data," said StorageMart chief marketing officer Tron Jordheim in an interview. "There are so many obvious variables and an unknown quantity of unknown or obscure variables." Even if you have been prudent about identifying variables, Jordheim said, "after you make the decision you have to know that you may [nevertheless] have missed a variable. You will be on the lookout for curious or odd results after the decision that indicate you missed a variable."

When retail promotions are more (or less) successful than expected, it may be because important variables are missing.

"In low-income areas, there tends to be more shopping at the first of the month, so stores may do promotions to fill in the valleys," said Ken Gilbert, professor emeritus at the University of Tennessee. "If you look at the relationship between promotions and sales, it would look like promotions hurt sales. To find out the real effect, you have to do [tests] where you experiment in high-demand or low-demand periods, or you control for the seasonality in demands."

(Image: Jodylehigh via Pixabay)

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jagibbons
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jagibbons,
User Rank: Ninja
7/24/2015 | 2:38:58 PM
Excellent summary of statiscally fallacies
These are the topics covered in most any business statistics course, but many still fall victim to them. I see the correlation versus causation problem a lot. Not sure if this is due to lack of education or experience or some sort of logical laziness that prevents some from digging further and asking critical questions. What is the old adage? 90% of statistics are made up... Don't make important business decisions without the right level of and the right kind of analysis.
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