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News Article Statistical Critique essay
← Financial Analysis: Nokia CorporationIn Defense of Prejudice →

News Article Statistical Critique. Custom News Article Statistical Critique Essay Writing Service || News Article Statistical Critique Essay samples, help

The given paper examines the article “Taming Volatile Raw Data for Job Reports” that is published in the New York Times. The article is written by Catherine Rampell. The author tries to debunk claims made by some people that data on unemployment, which had been released by the Bureau of Labor Statistics, was manipulated for political reasons. With the help of historical trends the author of the article tries to justify the data released by the bureau and deflate the claims made by some conservative experts. According to Rampell (2012), they claim that the data had been falsified in the interests of the democrats ahead of the elections that were held in November 2012.

The article explores one kind of data – the ratio scaled data. For example, when Rampell (2012) says that in September the amount of people who had jobs rose 873,000, this is the ratio scaled data. The intervals between all levels of the scale are equal, and there is an “absolute zero”; thus, it is possible to express meaningful ratios in this type of data (Ott & Longnecker, 2010). Rampell (2012) also uses percentages in the article, which also refer to the ratio-scaled data.

The measure of central tendency used in this article is mainly the mean. In a number of instances, the author quotes the “average” figures in her argument. For example, Rampell (2012) says that a gain in the number of people who had jobs averaged 164,000 for the previous months. This does not necessarily mean that each month posted a gain in the people who had jobs by 164,000. However, it does mean that although the figures could have been vastly different, their mean could be calculated to 164,000 (Kitchens, 2011). According to the article, “…the number of those 20 to 24 who had jobs fell by an average of 98,000 from July to August” (Rampell, 2012, p.B1).

In this article, Rampell has skillfully used statistics to make the case.  The author points out that the figures are enormously different from the expected trend and further explains that the survey had an error margin of 400,000. Rampell provides examples of historical averages to explain that there was no need to believe any manipulations. The article also employs the graphs to explain the swings in the levels of employment. The graphs reveal pendulous figures that swing wildly from one extreme to another, suggesting enormous changes in the levels of employment, which are the very core of Rampell’s argument.

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