Reference no: EM131434133

Statistics and graphics continue to be the most tools for approving and disapproving theories in the present times. Different people use statistics and graphics for other several reasons and in different industries including sensitive fields like medication and healthcare. However, these graphics and statistics can be used inappropriately either with or without knowledge to mislead people. There are different ways that statistics and graphics can be used to misinterpret data.

Differences between the causation and association is the first way of misinterpreting data. At times, researchers may conclude that one variable in a research cause another variable because the two variables affect each other (Okeh, 2009). It is not true that since one variable affects another that one of them should cause another. Therefore, concluding in such dimension is a way of data misinterpretation.

The second way is to involve biasness and personal opinions in collection and representation of data. To explain this, an example of unevenly distributed data can lead to misinterpretation through graphics by making some abnormal exaggerations. For instance, a researcher may want to display the buying trends of coffee in a certain state over the past five years.

During representation of data in a bar chart, some of the year may have insignificantly low sales that the researcher decides to increase the bar for that year to be visible. This action may lead to misinterpretation of data by the audience to which the data is represen

Errors is another way in which data can be misinterpreted. Errors in data analysis can lead to poor data representation thus leading to misinterpretation. Most individuals believe in statistics and when the graphs are drawn to present data, they tend to believe the trends regardless of the errors that were made.

Scenarios

Data misinterpretation exists in several fields including the competitive markets and industries like pharmaceutical and healthcare. In pharmaceuticals, more than one medication or drug exist in the market that may serve one purpose. To make a particular type of drug to standout, commercials are used with personal opinions in data analysis and interpretation (Okeh, 2009).

For instance, one of the commercials used small prints to display information about the guarantee of the benefits. In other instances, the benefits of one drug are represented in a larger scale as compared to the benefits of competing drugs to create intention misinterpretation of data by consumers.

Another example is the presidential election that was conducted in 1936 where predictions of the winner favored London. The polls were wrong because the winner was Roosevelt. The reason for this misinterpretation is the bias where lower income groups were excluded from the polls and taking too low sample size.