Stat Lab - Kelly Brock - Introduction Statistics can be...

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Introduction : Statistics can be defined as “the collection, organization, and analysis of numerical data” (Singer). This branch of mathematics has many uses in everyday life, such as analyzing surveys to determine the effectiveness of television commercials or using raw experimental data to predict the overall impact of a drug’s side effects. Today, surveys are one of the most common forms of statistically-analyzed data. One can also view statistics as an integral part of any scientific field, particularly in biology and the study of populations. Furthermore, this field has been useful to civilization for more than 5000 years. The Babylonians used a simplified form of statistics to keep track of trade, and the Egyptians also analyzed their wealth and populations around the same time. Even the Romans took censuses of their citizens and used the statistical information for taxation purposes. In the seventeenth century, Blaise Pascal and Pierre de Fermat explored the related field of probability as it related to gambling. The study of probability, which examines the effects of chance, led to a greater sophistication in statistical techniques. For example, William Sealy Gosset developed the student’s t-test under the pseudonym Student while he was working in a local brewery in 1908. The student’s t-test compares a calculated t-value to a critical value; if the t-value is greater than or equal to the critical value, one may reject the null hypothesis (a statement of no difference that defines what the t-test is supposed to prove or disprove). The purpose of this test is to determine the likelihood that chance accounted for the data results. Around the same time, Karl Pearson developed the chi square test, another measure of the significance of data that involves the attempt to reject a null hypothesis. This test relies on the assumptions that the data points are mutually exclusive and that the data are ordered on a nominal scale. He also developed the product moment correlation, a measure of how closely two sets of data are related to each other. Again, it revolves around the rejection of a null hypothesis by means of comparing a calculated
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value with a critical value. Finally, one last form of statistical test is called linear regression. Although the underlying theory was developed independently by Gauss and Legendre in the early nineteenth century, it was later expanded by mathematicians such as Karl Pearson and Francis Galton, a biologist. Today, linear regression is a widespread statistical test used to determine if one set of data is dependent on another set. In modern times, the field of statistics forms a basis of everyday life because it encompasses many different subjects. Meteorology reports, doctors’ case studies, and gambling are all closely associated with statistics. Despite its ancient roots, this branch of mathematics is still incredibly relevant today. Methods and Materials
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This note was uploaded on 10/12/2011 for the course BIOLOGY 100 taught by Professor Smith during the Spring '11 term at South Carolina Upstate.

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Stat Lab - Kelly Brock - Introduction Statistics can be...

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