Last week, you learned how to summarize a distribution by using frequency distribution tables and various graphs. The shape the frequency distribution forms is important, but frequency tables and graphs are not always the most efficient or meaningful way to summarize large amounts of data. In addition to graphing the shape of the distribution, statistics can be computed to provide even more information about the scores. Measures of central tendency tell us how the scores in the data set “hang together.” What score or scores are “typical” for the data set? Measures of variability help us understand how scores are spread out. How big are the distances between scores? Computing measures of central tendency, such as the mean, median, and mode, and measures of variability, such as the range and standard deviation, is often the first step researchers take after collecting data for a study.
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