teapaper_hagelst

teapaper_hagelst - Jessica Hagelstein March 15, 2005 BE.104...

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Jessica Hagelstein March 15, 2005 BE.104 In describing the evolution of statistical analysis in his book The Lady Tasting Tea , David Salsburg illuminates the ways in which the development of statistical analysis was shaped by the personal and political circumstances surrounding its pioneers. Each chapter focuses on the unique story of a particular statistical method and its discovery, providing the reader with a vivid sense of its temporal relationship and importance to the growing body of statistical knowledge. Though these discoveries are not always presented chronologically, a clear path emerges from abstract mathematics in the late nineteenth century to current applied statistics. Salsburg’s personal knowledge of many of the founding fathers of statistical analysis draws the reader into this tight knit community and adds to an understanding of the genius that brought the science out of the air and into the hands of millions around the world. He continually brings to light the pervasive nature of statistical analysis and its crucial role in so many, often unexpected, realms of contemporary society. Today, many statisticians work in epidemiology, the biomedical sciences or in the business sector, but the first statisticians were, in fact, mathematicians interested in solving abstract problems. While earlier discoveries in statistics were made by eighteenth century mathematicians like Gauss and Bernoulli (Salsburg, 16), it was not until the late nineteenth century that Karl Pearson and his rival, R.A. Fisher, began to elucidate the fundamentals. Pearson began by collecting massive amounts of data and analyzing them without a physical problem to solve, developing the first statistical model of a skew distribution and its four key parameters. Pearson believed, correctly, that his observations were a random sampling from the actual distribution that existed in nature, and this distribution, not the data themselves, was the important result (17). However, his error lay in the assumption that his four parameters could actually be determined, that they were the same for the distributions of both the sample and the population. As Fisher later pointed out, these parameters could only be estimated for the population distribution, never known (35). Yet, this did not diminish the importance of Pearson’s 1
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assertion that the key aspect of an experiment was not the data obtained, but their distribution, a basic tenet of statistical analysis. Pearson is also responsible for giving statisticians the chi-square measurement and the basics of hypothesis testing, both of which are heavily used today (96). Salsburg charts the animosity of these two men toward each other throughout their careers. Through his early work with crop variation data, Fisher developed sound formats for experiments, including randomization, as well as the role of variance and co-variance in statistical analyses (48). However, because of his earlier efforts to correct Pearson’s theories, Fisher’s work was consistently rejected
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teapaper_hagelst - Jessica Hagelstein March 15, 2005 BE.104...

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