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third stage It is at this stage that we require our best statistical techniques (Tukey, 1973/1986b, p 795) As a researcher moves through these stages, he or she moves from hypothesis generation to hypothesis testing and from pattern identi cation to pattern con rmation Whereas CDA is more ambitious in developing probabilistic assessments of theoretical claims, the exibility and bottom-up nature of EDA allows for broader application In many cases an appropriate model of parametric statistics may be unavailable for full CDA, while the simpler techniques of EDA may be of use Under such a circumstance the maxim should be followed that [f]ar better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise (Tukey, 1962/1986c, p 407).

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Summarization and the Loss of Information Behrens and Smith (1996) characterize the nature of data analysis as being oriented toward generating summary and extracting gist When faced with numerous pieces of data, the goal of the analyst is to construct a terse yet rich mathematical description of the data This is analogous to the summarization process that occurs in natural language processing After reading a long book, one does not recall every individual word, but rather remembers major themes and prototypical events In a similar way, the data analyst and research consumer want to come away with a useable and parsimonious description rather than a long list of data An essential concept associated with summarization is that every summary represents a loss of information When some aspects of data are brought to the foreground, other aspects are sent to the background.

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Algebra Lies, So You Need Graphics Anscombe (1973) described a data set of numbers, each measured on the scales of x and y He described the data as having a mean of 9 and standard deviation of 33 in x and a mean of 75 and standard deviation of 203 in y The data were t by ordinary least squares (OLS) criteria to have a slope of 5, an intercept of 3, and a correlation of 83 This allows the terse and easily interpretable summary for the data in the form y = 3 + 5(x) + error As a thought experiment, we encourage the reader to try to visualize a scatter plot that depicts such data If you imagined a scatter plot similar to that shown in Figure 2.

1, then you are quite correct, because this represents the data Anscombe provided that met the descriptive statistics we described previously This, however, is only a small.

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