From the representation of the information in the form of a database table (data matrix) to analyze some aspects of the variables under examination, a distribution of the frequency is necessary. Any process of data synthesis causes loss of information.
For example, on a distribution of individuals with a height between 140 and 170 cm it is not possible to trace individuals.
Other three of the most common data misinterpretation risks:
1) Correlation mistaken for causation: our first misinterpretation of data refers to the tendency of data analysts to mix the cause of a phenomenon with correlation. It is the assumption that because two actions occurred together, one caused the other. This is not accurate as actions can occur together absent a cause and effect relationship.
2) Confirmation bias: our second data interpretation problem occurs when you have a theory or hypothesis in mind, but are intent on only discovering data patterns that provide support, while rejecting those that do not.
3) Irrelevant data: the third and final data misinterpretation pitfall is especially important. As large data is no longer centrally stored, and it is inevitable that analysts will focus on data that is irrelevant to the problem they are trying to correct.
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