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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(Image: Geralt via Pixabay)

(Image: Geralt via Pixabay)

Data is misinterpreted more often than you might expect. Even with the best intentions, important variables may be omitted or a problem may be oversimplified or overcomplicated. Sometimes, organizations act on trends that are not what they seem. And even when two people view the same analytical result, they may interpret it differently.

"Statistics can tell you 'this versus that.' The real questions are, 'Is the difference worth worrying about?' and 'Have we collected enough data to allow us to make a decision?" said Ken Gilbert, professor emeritus of the department of statistics, operations, and management at the University of Tennessee, in an interview.

It is entirely possible for business leaders to obsess about something that is statistically insignificant, or for data scientists to omit important variables, simply because they do not understand the entire context of the problem they are trying to solve. In short, the path to valuable insights can include a number of obstacles, some of which may not become apparent until after the fact.

Some individuals and groups take a top-down approach to data analysis, meaning that they focus on the business problem they are trying to solve and they make a point of identifying variables that have been relevant in the past in a same or similar context. Others take a bottom-up approach, meaning that they attempt to correlate variables with that which they are trying to improve (such as website conversions or sales). The danger of the latter approach is a high probability that some correlations are statistically significant but are an artifact of the way the data has been analyzed, versus being an accurate indicator of underlying relationships, Gilbert said.

There are a lot of ways data can be misinterpreted, and business leaders need to understand how and why it can happen. Here are nine examples.

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio

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shamika
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shamika,
User Rank: Ninja
8/5/2015 | 10:23:24 AM
Re: statistic lies
I agree. Sometimes it can be the wrong statistical tools that has been used which will lead towards wrong analysis and decision making.  
LisaMorgan
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LisaMorgan,
User Rank: Moderator
7/30/2015 | 8:42:32 PM
Re: statistic lies
Thank you.
kstaron
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kstaron,
User Rank: Ninja
7/30/2015 | 8:19:24 PM
statistic lies
Nothing can lie quite like a statistic. The worst part is that sometimes you don't know when it's lying. Excellent article hitting the main ways statistics can give you bad information. It's an eye opener to know that even with massive amounts of big data, you can still get bad results if you make one of these mistakes.
jagibbons
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jagibbons,
User Rank: Ninja
7/25/2015 | 11:43:25 AM
Re: Excellent summary of statiscally fallacies
Agreed. If you don't understand the context of the data, it is certainly hard to make good decisions based on that data.
shamika
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shamika,
User Rank: Ninja
7/24/2015 | 11:05:14 PM
Re: Excellent summary of statiscally fallacies
@ jagibbons I agree with you. It is always important to use the statistical data analysis to reduce variations and provide accurate reports for decision making.
shamika
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shamika,
User Rank: Ninja
7/24/2015 | 11:02:30 PM
Sources of Variation
This is really important. Otherwise it will lead towards wrong analysis. It is always important to use the statistical data analysis mechanisms in order to avoid those.
shamika
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shamika,
User Rank: Ninja
7/24/2015 | 11:02:26 PM
Re: Excellent summary of statiscally fallacies
This is an interesting article on data misinterpretation. As the first point explains domain expertise is something very important when dealing with data. Lack of this could lead towards wrong statistics and wrong decision making.
LisaMorgan
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LisaMorgan,
User Rank: Moderator
7/24/2015 | 4:09:37 PM
Re: Excellent summary of statiscally fallacies
Amen.  :-)
jagibbons
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jagibbons,
User Rank: Ninja
7/24/2015 | 3:56:13 PM
Re: Excellent summary of statiscally fallacies
Agreed that this level of maturity will take time. The sad fact is that many businesses don't have the time. They must make business decisions quickly, and in many cases they are making decisions on a partial or complete misunderstanding of the very data they are using the make the decision. The best any of us can do is be aware of our limitations and seek assistance to help us move forward and learn.
LisaMorgan
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LisaMorgan,
User Rank: Moderator
7/24/2015 | 3:47:00 PM
Re: Excellent summary of statiscally fallacies
Thank you.  My personal take is that people are really busy and if this kind of critical thinking isn't baked into your DNA yet, then it hasn't become part of the integral thought process yet.

A lot of business majors took statistics courses because they were either required to do it or it seemed less scary than some other math requirement.  In the absence of knowing why statistics matters to your job - which was clearly absent at least when I went to school - unless you consider math fun as some of us do, it's just a requirement you tick off your undergraduate to-do list and move on.  It does not become part of your thinking.  It doesn't interest you, it doesn't intrigue you.

I'm encouraged that colleges and universities are re-examining their curricula because data is becoming so integral to all parts of the business.  If even basic statistics is something your future employer is going to require, it behooves you not to just get an "A" in the class, but internalize what it means in terms of your major.  

Smart businesspeople ask smart questions.  Once people figure out that "smart" behavior includes asking the questions you outlined, more will be motivated to do that.  No one is born knowing any of this stuff.  OTOH, people have to adapt.

Sure, I imagine some people might be lazy about it or consider asking another layer of questions Yet Another Thing to do.  If you're responsible for outcomes, you're wise to ask such questions.  Even boards of directors are starting to ask questions they never asked before such as, "What's your Big Data strategy."

I think it's a matter of industry maturity and professional maturity.  It took time for the general population to become computer literate.  It will take some time for more people to become data literate, although this will happen faster, IMHO.

Love your points.
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