Essay Sample on Curbing Misinterpretation of Data

Published: 2023-02-21
 Type of paper:Â Essay Categories:Â Knowledge Data analysis Statistics Pages: 2 Wordcount: 440 words
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Many people frequently misinterpret statistical data if they lack the expertise or skills how to interpret it even with the good intention they might find themselves altering the information unknowingly. Some of the variables may be omitted by the researchers during the process of data correction (Chryssolouris, Dicke, & Lee, 2014). Similarly, the problem might be overcomplicated by the researchers, therefore, leading to misinterpretation of the data.

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In order to avoid problems related to data misinterpretations, there should be a creation of awareness to other professionals on matters related to statistical data. In a business set-up organization, most of the employees have business-oriented minds; therefore, they are more of domain expertise but not data scientists (Chryssolouris et al., 2014). They should be provided with skills and knowledge concerning data interpretations so that they can be aware of the variables they should look for to avoid the omission of variables once the statistician brings them data for analysis; therefore, cases of misinterpretations will be reduced.

Important variables should be included in the data. The omission of fundamental variables during data correction leads to misinterpretations of data, which eventually leads to defective conclusions after analysis and imprudent investment in a business setup (Chryssolouris et al., 2014). Therefore, it's the role of the researcher during the process of data correction to ensure the important variables are included in the data.

Data is usually collected in a raw format; therefore, it's hard to derive information from raw data. Data is consequently simplified, analyzed, and represented in a format that information will be easy to be derived. Data can be represented in the form of textual, graphical forms, or even inform of tabular (Chryssolouris et al., 2014). To accurately represent the data, the researcher should use the simplest method of describing the data, and the most effective representation of data is the use of the graphical format. Graphs are the most accurate and easy to interpret.

To ensure people interpret data correctly, they should be provided with a guideline on the important variables that are included in the data. Not all people are data scientists; therefore, without a critical guidelines, they might misinterpret the data. Secondly, the data should be simplified, where possible, since people might find it hard when complex. It should be represented in a well-labeled graph, which is the best and most accurate method of representing data. Finally, the data collected should be meaningful. For people to interpret correctly, they should have meaning and making sense. Otherwise, complex data might be misinterpreted by most domain experts.

References

Chryssolouris, G., Dicke, K., & Lee, M. (2014). On the resources allocation problem. International Journal Of Production Research, 30(12), 2773-2795. DOI: 10.1080/00207549208948190

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