comment from post 2

Regina , it is nice the way you explained how statitics can be used to misrepresent data in advertising but also i can add that graphs that are ambiguous or cluttered do not allow clear interpretation of results, which increase chances of misrepresentation. Absence of labels, scales, and data elements also increase errors in data representation and mislead the reader (Brotton et al., 2010). A typical example of data misrepresentation is the one commonly observed during promotional activities of a new drug into the market. Often, it could be observed that the clinical trials performed and results obtained on the use/significance of the medicine are unnecessarily augmented to highlight

quality or effectiveness of the medicine. However, the medicine may be less effective or might contain adverse side effects that the researchers or company are aware, which is never mentioned in reality. In some situations, pie charts, the most easily understandable graphics, are used to represent results in studies conducted with incomplete data or missing variables (Pavlovich-Danis, 2012).

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