On 16 Mar 2002 08:11:26 -0800, [EMAIL PROTECTED] (wuzzy) wrote: > The highest form of research is that which finds a significant > treatment effect in vivo. > In my experience, though, It seems that small effect sizes are almost > the rule in natural environments. Single variables might often > account only for 3%-5% or less of observation: probably because there > are so many variables operating as well as many of them being > transient/saltatory/unmeasurable. >
You have been bamboozled! Where did you get your notion of 'effect sizes'? What *is* your notion of effect sizes? - you seem to have internalized Cohen's rhetoric, which is suitable to his audience: social scientists designing experiments with 20-100 subjects. Bigger 'effects' -- There are good scientists in labs who never do more than 3 or 5 replications (say) because everything fuzzier is too dubious. Much smaller 'effects -- There are nuclear physicists who now scan the tracks of millions of collisions in order to compare a handful of special events. Later, you mention r. The Pearson correlation is useful for describing things with high correlations. It is *not* especially useful for low correlations. It is especially *bad* for describing rare occurrences. Epidemiologists use "odds ratios". For instance, evidence of correlation of a cause with a disease might be given by an odds ratio of 10, whereas the corresponding Pearson r is, say, 0.01. That takes a big N to be 'significant', of course. Across a certain range of prevalences, you can have the same Odds Ratio, and the r will increase directly with the rates of disease: and that is why r is properly disregarded by *them*. "Small effects" in that traditional sense of r and percent of variance is why a number of epidemiological studies decide to enrol ten thousand or more subjects and follow them for years. > At the same time there is a trend in stats to move away from p-values > to just saying (as dichotomy) "significant" "or not", and quoting a > confidence interval.. - actually, the trend is *away from* the simple *test* that said significant versus not, and toward giving the exact p-value since that is more informative. The CI is an alternative presentation of the exact p-value, you may note, which happens to provide more detail: the two can be computed from each other, but you also need to have the mean and SD along with the (suitably exact) p-value. > > Anyway, my question is one that I have asked before: are you able to > draw conclusions from research based on such small effect sizes> an r > of +0.08 that is significant. I have found alot of regulatory > mechanisms operate with effect sizes that are this small: ex. there > may be a feedback mechanism that is well established yet the effect > size is only 0.08 > > any articles or books on this topic appreciated.. Look to the books on research or experiments or experimental design in your own area? -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
