9 Causes Of Data Misinterpretation

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.

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.

Source: http://www.informationweek.com/big-data/big-data-analytics/9-causes-of-data-misinterpretation/d/d-id/1321338?

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