A frequent misunderstanding is that statistics gives a degree of proof that something is accurate. As an alternative, statistics provide a measure of the probability of observing a certain outcome. It is easy to mistreat statistics analysis even to the point of error because statistics do not familiarize us with organized or systematic error which can be carried into the data deliberately or unintentionally. There are many related variables in statistical numbers that the individual analyzing the information does not perceive, and without additional clarification or supportive data, one can simply assume to the wrong hypothesis and the scientific data could be exhibited as facts rather than chance. If the origin from which the data was collected was not accurate, then this will reflect a statistic that is false, partial, and based on incorrect information, but those individuals who may perhaps later understand that the data source was not accurate, and as a result incorrect data is publicized. Because statistics deals with facts and figures it often seems to be more conclusive and less doubtful of wrong claim than factual arguments, but facts can be easily swayed in favor of someone’s views.
That alone provides a great source of credibility to the paper. The idea that this is an author who has done the research, gathered the numbers, and analyzed the data, allows the reader to rest in the idea that they are reading a valid article, and receiving good, hard, evidence. Twenge also uses a very logical tone throughout her article, maintaining the idea that the data is as clear as day, and that there is no disproving it; the numbers show true facts.
As a way to communicate additional information to the audience, Barbara Ehrenreich provided statistical data in the footnotes of certain pages. Although these statistics are not
Hard numbers and statistics in combination with logical flow prove to be the foundation for the article’s ethos. Since the author is suggesting that something is happening that we can’t see, he has to use what we can see to get to that conclusion. He successfully does so by comparing numbers from reliable agencies and then going through the logical implications of how they changed. The article follows a logical flow that the reader can easily understand, thereby giving him a credible argument.
Even if a researcher has mountains of data, unless he carefully scrutinizes and questions all information, digging up potential lurking variables and possible bias, he can be confounded. If a reader can glean any lesson from Freakonomics, it is this—always look at every piece of evidence as closely as possible. Stare at it until eyes begin to bleed. Yank up confounders by their roots. Take the time necessary to make sure conclusions are draw correctly. Levitt spent hours researching his questions. Sometimes he failed, as with the abortions. Sometimes he triumphed, as with the
The August 1999 article in the American Psychologist discusses proper statistical methods and how they should be utilized in journal articles. Using some of the guidelines put forth in the article, I will attempt to show the extent to which Bach & Bach (1995) follow these principles.
In Schors book she states that the statistics presented are her estimates and calculations. Oddly in The Lonely American they are said to be “government statistics” rather than a compilation of government statistics and personal research. The fact that Olds and Schwartz do this causes their own research to be discredited. When the authors present the information in the following way:
Statistics is defined as “the science that deals with the collection, classification, analysis, and numerical facts or data” (Dictionary.com,2012). Sometimes it is important to analyze statistical data in order to understand how something works or doesn’t work. In the case of American public education, there is tons of statistical data being thrown around, but what do all of these numbers really mean? How does this data help us? Although statistics provide clarity for constant scrutiny to the public education school system, they also help us to understand what were doing wrong in the classroom. In comparison of two different states, Nevada and Wisconsin lay at two very different ends of the educational spectrum. To properly understand how Wisconsin is far more successful in terms of academic achievement than Nevada, it will be helpful to compare and analyze what contributing factors effect both states in terms of their educational reputations.
* For a great cautionary tale of statistics and their manipulation go to this page on the Web: http://www.ifeminists.net/introduction/editorials/2004/0324.html
Statistics are commonly used to support peoples’ position in the argument over gun ownership. I intend to do no less. Consider this statistic; in 2008 roughly 10,886 murders were committed with a firearm. This is a valid number on which we must reflect. Imagine if the 10,000+ victims had guns of their own. That statistic could have shockingly stayed the same, just with different victims: the criminals.
The following article analysis review by Team B illustrates and identifies several examples of statistics abuse in the practical world as a result of flawed research. The following examples demonstrate how a manger could and in many examples, does make erroneous decisions due to inaccurate statistics. The team has compiled the results by detailing the respective articles.
numbers to influence public opinion. Stone discusses how “Numbers are used to tell stories… [and] the power to measure is the power to control. Measures have a lot of discretion in their choice of what and how to measure.” This can become very dangerous because when politicians present the public with data, they could present as much or as little data as they see fit and then they utilize that selective data to tell stories to sway public opinion.
The father of quantitative analysis, Rene Descartes, thought that in order to know and understand something, you have to measure it (Kover, 2008). Quantitative research has two main types of sampling used, probabilistic and purposive. Probabilistic sampling is when there is equal chance of anyone within the studied population to be included. Purposive sampling is used when some benchmarks are used to replace the discrepancy among errors. The primary collection of data is from tests or standardized questionnaires, structured interviews, and closed-ended observational protocols. The secondary means for data collection includes official documents. In this study, the data is analyzed to test one or more expressed hypotheses. Descriptive and inferential analyses are the two types of data analysis used and advance from descriptive to inferential. The next step in the process is data interpretation, and the goal is to give meaning to the results in regards to the hypothesis the theory was derived from. Data interpretation techniques used are generalization, theory-driven, and interpretation of theory (Gelo, Braakmann, Benetka, 2008). The discussion should bring together findings and put them into context of the framework, guiding the study (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). The discussion should include an interpretation of the results; descriptions of themes, trends, and relationships; meanings of the results, and the limitations of the study. In the conclusion, one wants to end the study by providing a synopsis and final comments. It should include a summary of findings, recommendations, and future research (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). Deductive reasoning is used in studies...