What are Damned Lies and Statistics?
Joel Best’s Damned Lies and Statistics is a book all about recognizing statistics that are legitimate and others that are really quite horrible. The goal of this book is not that the average every day person be able to read a statistical table from a scholarly journal, but rather that anyone could personally value a statistic he or she may come across in a newspaper article or on a news program. Best was essentially effective in achieving his goal; however, he was effective to the point of overdoing his job of showing that there are bad statistics which give readers cause to evaluate them outside of hearing them on the news.
Best begins his book by discussing the importance of social statistics. He notes
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Comparisons over time may be inaccurate due to changing measures, unchanging measures, or projections. Comparisons among places can go wrong not only in different countries, but in rural and urban areas as well. Making comparisons among groups can also be comparing apples and oranges, as people with different socioeconomic status, ethnicity, or religion cannot be compared as simply as desired. Even social problems cannot be compared to each other, because no two are the same and sometimes aren’t even similar.
“Stat Wars” is the title of Chapter 5, and it describes the process of conflicts over such social statistics. There are debates over particular numbers, data collection, and statistics and hot-button issues. Knowing the causes of bad statistics outlined in Chapter Two will help readers in such stat wars.
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
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For social sciences majors, reading this book can provide a look at exactly what biases there are in the field we are hoping to enter, and what mistakes are commonly made. It provides a long list of examples of ways in which numbers can be messed up, and is a good warning to those of us wanting to be social scientists. I was able to take from it the knowledge of how to be critical of statistics that I see and I think that it has complimented what I have learned in some of my Political Science classes, including this one. Even though I am not exactly the audience member Best was trying to reach, he was at least successful in showing me what I have learned by just being a part of the social sciences, that sources of statistics and facts are biased and that it takes effort to be an informed member of this society. This is a useful thing to know, and hopefully around all of the examples and repetition, readers of all types can come to this
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
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
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.
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.
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.
Ferguson, Andrew. "Are Americans Closet Statists?." Weekly Standard, 09 Aug 2010. Web. 7 Dec 2010.
There is also criticisms to the social surveys as both studies failed to take into account of such variables as sex, age and ethnic origin. Once these are inserted, the correlations identified by the researchers tend to disappear.
Mcpherson, Gareth. "Statistics." United Fathers of California. Cambridge Newspapers, 6 Mar. 2013. Web. 25 Nov. 2013.
When we are introduced to statistics we either face it or deal with it head-on despite our fear with this subject and we start thinking about the time it would take us to complete a paper or statistics design bases on the extended reading we would have to do in order to understand the subject for clarification of what to expect, and take away from that subject. Therefore, this discussion will define confidence intervals, stipulate when we would need to use confidence intervals in statistical analysis, and examine why the Publication Manual of the American Psychological Association recommends the inclusion of confidence intervals in study results.
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...
* 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
Babbie, E. (2007) The Practice of Social Research. Thomson Higher Education. Belmont. (USA) Eleven Edition.
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.
This chapter taught me the importance of understanding statistical data and how to evaluate it with common sense. Almost everyday we are subjected to statistical data in newspapers and on TV. My usual reaction was to accept those statistics as being valid. Which I think is a fair assessment for most people. However, reading this chapter opens my eyes to the fact that statistical data can be very misleading. It shows how data can be skewed to support a certain group’s agenda. Although most statistical data presented may not seem to affect us personally in our daily lives, it can however have an impact. For example, statistics can influence the way people vote on certain issues.
Parsons, Talcott. (1938). The Role of Theory in Social Research. American Sociological Review. 3(1), 13-20.