hirsch;180007 Wrote: > Nope. You're back at the basic error. How do you distinguish the results > obtained in a negative DBT from those obtained in a hearing-impaired > sample? > > Let's take a basic perceptual test. We test for a difference between > two stimuli, and try to figure out whether or not we heard a > difference. We then do statistics, and figure out whether or not the > significance, a statistic called alpha, is less that 0.05. This number > is a probability. It means that if we say the difference is real, the > odds that we're wrong is less than 5% (or 19 to 1 odds). Note that if > the odds that a difference is real are something like 3 to 1 in favor > or the difference being real, we will still determine that the > difference is not significant. That is because the probability of > committing a Type I error (saying a difference is real when in fact it > is not) is higher than we will accept. This is where the problem comes > in, as the failure to obtain a significant alpha says nothing about a > negative result. In science, the failure to obtain significantly > significant differences can still mask real differences, and in fact > sometimes the odds favor the existence of such differences...but not by > enough for us to accept them as "real". The normal scenario is to run > the test, fail to obtain alpha less than 0.05, and then jump to the > conclusion that since we didn't get a significant difference, there > isn't one. Wrong. > > The converse of a Type I error in statistics is a Type II error (saying > that there is no difference when in fact there is a real difference). In > order to make statements about negative results, we need to compute a > statistic called beta (probability of committing a Type II error, or > saying that there is no difference when in fact a difference is real) > which needs to be below 0.05 before we can attribute any meaning to a > negative result. Figuring beta is complicated, and cannot be done > without some sort of a priori power analysis (which determines just how > big an N is needed to make sense of failure to achieve a significant > alpha). If you have not computed beta, a negative result has no > meaning in a statistical sense. To "prove the negative", you need to be > able to calculate the odds that your conclusion is wrong, the same as > for a positive result. It's a lot harder for a negative, however. > > Note that blinding is not even mentioned in the above. It's simply a > way of removing a confounding variable so that a significant alpha > becomes more interpretable. That's it. If you think what I'm saying > is in any way false, I strongly recommend reading a book on > statistics/experimental design.
You are correct regarding statistical significance. However, if you take one specific individual who claims to hear a difference, and that specific individual cannot identify the difference in a DBT, then there is an extremely high probability that that specific individual does not really hear a difference. Conversely, if a different specific individual could differentiate in a DBT, there is an extremely high probability that the difference is audible, even though the first specific individual could not hear it. Statistical significance is all well and good, but I would suggest that the former individual should refrain from spending the money to make the change that he cannot hear. Further, I would suggest that finding at least one individual who can differentiate in a properly conducted DBT should satisfy most reasonable objectivists, even though there is a higher probability of a false positive than there would be for a larger sample size. -- jeffmeh ------------------------------------------------------------------------ jeffmeh's Profile: http://forums.slimdevices.com/member.php?userid=3986 View this thread: http://forums.slimdevices.com/showthread.php?t=32352 _______________________________________________ audiophiles mailing list [email protected] http://lists.slimdevices.com/lists/listinfo/audiophiles
