Thesis on "USA World Bank"

Thesis 11 pages (3179 words) Sources: 5 Style: APA

[EXCERPT] . . . .

USA World Bank Case Study

From the development of new psychological treatments to the selection of the President of the United States, statistics have been used throughout history in order to cast predictions that helped progress the scientific, social, and political climate of the world. In addition to their traditional niche in scientific genres such as psychology, medicine, and political science, statistics have recently been used to aid the research and development of products and services in the business community. Although statistics can be extraordinarily beneficial to fields from medicine to business, they can also be misleading. By choosing an insufficient sample, allowing data to lapse, or making a myriad of other errors, companies and researchers can come up with data that they believe is infallible, although it is ultimately incorrect. In the case study involving the U.S.A. World Bank, Bea pointed out several instances in history when this was the case. One, the instance of the 1936 election in which Literary Digest predicted that Alf Landon would win the presidential election by a landslide using fallible data. When FDR won the election, they discovered their error and were largely embarrassed. In this case, the error was the sample size. Although the Literary Digest had used a much larger sample than the Gallup Pole, their sample polled those who were considerably more well off than what was representative of the population, a demographic that tends to vote Republican. Although the data gathered by Literary Digest was incorrect, it is easy to understand how the researchers came up with this conclusion, seeing that they polled the sector that was most likely to vote for the Republican candidate. Similarly, Bea mentioned another case, the 1948 election, in which most polling companies stopped their polling activities based on the magnificent lead Thomas Dewey had over Harry Truman two months before the election. According to Bea, however, progressive strategies by Truman in the last month of his campaign turned the population in his favor, but because the polls were more than a month old, most of the polling agencies had incorrect data. Because the agencies stopped their polling prematurely, they faced a time fallacy, drawing conclusions on old sets of data.

Although these two examples of errors involving statistical research might be some of the more famous ones, they are certainly not the only time that statistics seem to point to one connection, while no connection or another connection is really present. Some examples of this include the statistics used in newspapers, journals, and magazines. Although authors may suggest that statistics suggest a certain connection, one cannot assume that correlation equals causation, or that a correlation even exists if the way that the statistics were developed. A more modern example involves the 2008 presidential election. According to a recent poll, 87% of those who watch Fox News are likely to support John McCain ("News you Watch Says a lot" 2008). While this poll is a good way of identifying what political parties watch what networks, and this is what the poll was intended to do, it would not be a good measure of the number of total news watchers that are likely to vote for the Republican candidate. If one were to use this data to suggest that 87% of news watchers would vote for John McCain, they would be using a sample error, seeing that most of those who watch Fox News tend to be Republican, and the programming on the network is often geared toward Republicans.

Understanding both the power and the problems with statistics, such as those discussed above, is a necessary prerequisite before using the tool to make business decisions. Unfortunately for USA World Bank, and especially Mary Monroe, the Vice President of New Product Development, who has a tendency of doing a thorough review of the research that she requires. Thankfully, the U.S.A. World Bank has been given the opportunity to assess their statistical data and make changes before they must make a decision regarding which product to implement. Through a brief description of the history of statistics and business and a description of the problems in USA World Bank's statistical data, one can determine solutions that will allow them to use statistics in order to launch the product that will most effectively bring them new customers and satisfy their current customers without alienating current customers in a different demographic, either small business owners or consumers.

Brief History of Statistics and Business

Although its origins date back to the seventeenth century, the exact philosopher who first began to discover the principals of statistics is unknown. In 1654, letters between mathematicians Blase Pascal and Pierre de Fermat discuss the concept that would become the theory of probability, and in 1656, German philosophers and mathematicians contributed to the discovery and development of its theories by adding the developing statistical theories to the genre of mainstream mathematics. After the original publication on statistics, written by Christiaan Huygens, the 18th century saw a great deal of exploration and publications on the theory of probability, and the concept of observation and combination in probability gained some popularity at the end of that century. In the 19th century, work on statistics expanded from simple probability to other more complex theories, including the method of least squares, probability errors, quartiles, regression, etc. (Verduin 2007). In the 20th Century, statistics had become a mainstream mathematical study, taught in high school and college classrooms. Mathematicians continued to explore the theories related to statistics, however, and spent much of the 20th century wrapped up in a debate over the correct methods of statistical practice (Verduin 2007).

While this short history suggests that the study of probability and statistics has a background in philosophy and inquiry like so many other important mathematical theories. The history of statistics' use in business, however, is much different. In the business world, statistics were born out of competition. According to Arsham, "in a competitive environment, business managers must design quality into products, and into the process of making the products" (2008). The way that that quality is designed is through statistics and statistical experiments, which remove "hindrances" at each stage of development and allow the researchers to develop quality products that are most likely to satisfy its customers (Arsham 2008). According to Arsham, "today's good decisions are driven by data" (2007). Instead of making decisions based on business politics or personal feelings, the implementation of statistics into the practice of business allows managers and CEOs to make choices that are based on hard data rather than how each board member feels.

Although the use of statistics in business reduces the chances of making errors due to personal belief in each stage of development, Arsham warns business statisticians to use the research they develop in a cautionary way. Statistics is based on variation; people's opinions change; and businesses cannot control the market. Although the history of statistical data's use in business allows managers and CEOs to have a more informed idea of their target audience through product development, it does not nullify the fact that statistical errors, like the two previously mentioned, can occur, or that statistics is not a surefire method of predicting customer or consumer behavior.

Description of the Problem at USA World Bank

Based on the earlier description of problems with statistics and the history of statistics within the business world, one can identify USA World Bank as a company that understood the value of statistical research in predicting which product customers were more likely to enjoy. However, they were not a company that truly understood the background of statistics, its risks, and the errors that can occur when statistical research is not precise. That is, they could not be classified as such a company until Bea joined the board of directors. While Bea pointed out several problems with the company's statistical research, further problems associated with the data gathered both by Mary's Best Market Research, and Jim's independent data exist. A summary of the most significant problems with the data is as follows:

Best Market Research's Sample Data and Timeliness

As Bea suggested, the sample collected by Best Market Research cannot be considered representative of the total population because of several demographical errors. For instance, in every state but Nevada, females out number males by only a slight amount, but the sample used to derive USA World Bank's data surveyed slightly more males than females (U.S. Census Bureau 2008). Furthermore, while only 8.6% of the population had earned a bachelor's degree or higher in 2006, the year from which the latest data was available, the sample population surveyed by Best Market Research is characterized by a stunning 17.1% having attained a bachelor's degree, 6.3% having attained a master's degree, and 1% having attained a doctoral degree (U.S. Census Bureau 2008). While only just above six percent of the population is identified as part of the age range thirty to thirty-four-years-old, and only just over seven percent could be called thirty-five to… READ MORE

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