A Jeanne Site
California State University, Dominguez Hills
University of Wisconsin, Parkside
Latest update: December 6, 1998
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Definitions of Descriptive and
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For the following material, I relied on Statistics, Fifth Edition, by Joseph F. Healey, Wadsworth Publishing Co., 1999. ISBN: 0-534-55260-9. This is not to be construed as an endorsement of the text. It was sent to me for consideration as a statistics text. I like it. It covers rather more than I think we can presently include in our course. But it is clearly written, and includes examples on SPSS. We will further consider it for adoption. You can review it in my office. jeanne
Descriptive statistics comprises the kind of analyses we use when we want to describe the population we are studying, and when we have a population that is small enough to permit our including every case.
For example, we might want to describe a physics class and compare it to a class of English literature. We might want to compare the gender composition of the classes, the math attitudes of the two classes, the familial demands and supports of the two classes. Descriptive statistics would allow us to do this.
The important keys to the difference between descriptive and inferential statistics are the capitalized words in the description: COULD DESCRIBE, COULD NOT CONCLUDE, AND REPRESENTATIVE OF.
Descriptive statistics can describe the actual sample you study. But to extend your conclusions to a broader population, like all such classes, all workers, all women, you must be use inferential statistics, which means you have to be sure the sample you study is representative of the group you want to generalize to.
This means you can't do a study at the local mall and claim that what you find is valid for all shoppers and malls.
You can't do a study on college sophomores and claim that what you find is valid for the general population.
You can't give a women's movement that includes a majority of a single ethnic group and claim that what you find is valid for women of all ethnic groups.
As you can see, descriptive statistics are useful and serviceable if you don't need to extend your results to whole segments of the population. But the social sciences tend to esteem studies that give us more or less "universal" truths, or at least truths that apply to large segments of the population, like all teenagers, all parents, all women, all perpetrators, all victims, or a fairly large segment of such groups.
Leaving aside the philosophical and methodological soundness of such a search for some kind of general conclusion, different statistical approaches must be used if you aspire to generalize. And the primary difference is that of SAMPLING. You must choose a sample that is REPRESENTATIVE OF THE GROUP TO WHICH YOU PLAN TO GENERALIZE.
Tests of significance are about this problem of generalization. A Chi-Sqaure or a T-Test tells you the probablility that the results you found in the group you studied are representative of the population that group was chosen to represent. Put in other terms that you will hear frequently, Chi-Sqaure or a t-test gives you the probability that the results you found could have occurred by chance when there is really no relationship at all between the variables you studied in the population.
You can find material covering this topic in Healey on pp. 7-9.