On Tue, 18 Dec 2001 14:19:34 +0000 (UTC), [EMAIL PROTECTED] (Benjamin Kenward) wrote:
> > Let's say you have a repeatable experiment and each time the result can be > classed into a number of discrete categories (in this real case, seven). > If a treatment has no effect, it is known what the expected by chance > distribution of results between these categories would be. I know that a > good test to see if a distribution of results from a particular treatment > is different to the expected by chance distribution is to use a > chi-squared test. What I want to know is, is it valid to compare just one > category? In other words, for both the obtained and expected > distributions, summarise them to two categories, one of which is the > category you are interested in, and the other containing all the other > categories. If the chi-square result of the comparison of these categories > is significant, can you say that your treatment produces significantly > more results in particularly that category, or can you only think of the > whole distribution? Mathematically, the statistical test is okay: There is no problem if you decided at the outset that 7 categories should be scored as D-and-not-D, so you would do a 2x2 contingency table test. Other inferences, of course, are more problematic. "Multiple-tests." Deciding on a test based on the outcomes is a form a cheating in the hypothesis testing, if you don't take that into account in the reporting of it. If your overall test is significant -- with 6 d.f., I think -- then it is somewhat conventional to look at the separate contributions by cell, without being too shy. If the overall test is *not* that happy, then you ought to state that, and offer further guesses as purely exploratory or suggestive numbers. Then you can describe one cell's contribution "versus the others." -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
