Misrepresentation of Data and Conclusion Using Statistics


Dataand conclusions can be misrepresented using statistics in many ways.The data collection can be biased so that the researcher uses his orher judgments to select those to interview and give thequestionnaires. The conclusions made then will be subjective lackingthe elements of objectivity in data. The conclusions made from thestatistics can also be misrepresented in such a way that theresearcher ignores the negative data that are contrary to his/herviews to make conclusions basing on the positive data obtained inorder to maintain his or her objectives. For example a company candecide to eliminate facts obtained from the consumer views contraryto its publicity and use the ones that reinforce its positive image(Anderson, Sweeney &amp Williams, 2012).


Ithink data should be analyzed basing the argument on the type andquality of the sites on which the data is to be collected. There is adifference of the data from different regions and the elements foundin such regions for example data analysis on the health careservices to the tribes of a country differs from the data analysis onthe geographic study like age. The data therefore should be collectedand analyzed with objectivity and validity(McCune, 2010).&nbsp


Qualitativedata indeed is meant to further the understanding of the existingreasons. It is meant to provide insights of a problem and coming upwith some idea and hypothesis which will then form the basis for aquantitative research which basically quantifies data and givesgeneral results from data obtained from a sample. It aims atmeasuring the opinions and concepts and the results further exploredusing the qualitative research (Spiegel&amp Stephens, 2011).&nbsp


Anderson,D. R., Sweeney, D. J., &amp Williams, T. A. (2012).&nbspEssentialsof statistics for business and economics.Mason,OH: South-Western.

McCune,S. K. (2010).&nbspStatistics.New York: McGraw-Hill.

Spiegel,M. R., &amp Stephens, L. J. (2011).&nbspStatistics.New York: McGraw-Hill.