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In Reply to: RE: statistics question posted by bjh on June 25, 2009 at 13:04:51
Meaningful significance and statistical significance are quite different. One can get very great statistical significance what a large random sample while have no impact on meaningful significance. A random sample of 25,000 would be sufficient for belt size to have a statistical significant impact on how people vote with no meaningful significance.
"""Meaningful significance and statistical significance are quite different. One can get very great statistical significance what a large random sample while have no impact on meaningful significance. A random sample of 25,000 would be sufficient for belt size to have a statistical significant impact on how people vote with no meaningful significance."""
But belt size is a continuous variable. We are talking about an ABX test, in which the answers are binary; true/false. Aren't these different topics?
This would only matter were you trying to say that all people can hear a difference and were using a random sample. As I understand it, you are only seeking to satisfy yourself, not to generalize. You seek to only say that it is improbable not that it is statistically significant. Inferential statistics are quite different than descriptive statistics.
Could you explain one more thing? I am aware that one can find correlation without proving causation. Seems like this issue is irrelevant to ABX testing.
Let's say in an ABX test I have a slight tendency to pick A. Because X is totally random in each trial, there is no way this can influence the results. That's my understanding.
easier to hear and better.
I must say that personally I am okay in just putting cables in and hearing a difference that I like. When I am studying whether people vote their party loyalty or whether states with concealed handgun laws have less crime, I am engaged in science and must be concern with causation, explanation, and whether the data are valid for the questions I am asking. When I am deciding whether one set of cables are better than another, I am not engaged in science. I am assessing my tastes in sound. The magnitude of the improve become important. Often I try blind tests if the improvement is small, but often if it is small I just stick with what I have.
Depending on how you conduct your testing, how thorough and rigorous you are, - your hypothesis and conclusions may vary. And some may call your testing methodolgy "poor science," - but it's still science. I have take issue with the neither those that that want more rigor or less; but, - I always appreciate tolerance for both sides.
Both sides of the river, there is bacteria; there must be meaning behind the moaning, is this living?
minded. When engage in information gathering to assess regularities of some benefit to society, I insisted on valid measurement of concepts, random samples, careful methodology, and care to avoid spurious relationships, when you cannot do real experiments. How I would love to randomly pick 25 states to have concealed handgun laws and 25 none and wait 20 years to see what differences there are between the states. My null hypothesis would, of course, be no differences.
I think it helps a lot that I am the whole population and that even if I'm wrong, there is little downside.
It seems to hit the fan each time someone posts that they did thus and so with good results. To someone else that may seem the depths of impossibility so they ridicule the poster rather than either trying it or just deciding that it's so unlikely that they aren't going to waste the time to check it out. Ridiculing others rather than thoughtfully examining your own understanding is very tempting and I've fallen off the wagon a few times myself.
I enjoyed this thread but like you, I believe, it's hard for me to see how statistics have much value for an individual listener. If I can't hear it, it doesn't matter and if I can I'll try to choose the best compromise if it isn't clear-cut. And I may share the result. Even if it isn't reliably predictive it does provide insights into things to try. And that's where AA shines, getting ideas to play with.
If I want to learn more about the underlying processes then I'd turn to measurements and try to find ones that correlate to the listening and from there try to reproduce the results with known changes which would hopefully be enough information to understand and usefully model whatever the process is.
Ironically even if it can be proven beyond question that Joe reliably hears a difference by putting marbles under his clock radio, that chunk of data alone adds little more to predicting my results than just his assertion that he does. One of the nicest things about this hobby is that you can try this stuff at home without the neighbors knowing.
Rick
a
"Meaningful significance and statistical significance are quite different."
An essential distinction.
There are two steps involved in going from statistical significance to meaningful significance . The first step bridges the gap from correlation to causation . The second step bridges the gap between an effect and a meaningful effect . Both steps are frequently contentious, as can be seen in numerous threads in this forum. The first step can be accomplished with a causal model, the second step requires a set of values.
Those people who view the world in terms of crude (e.g. black or white) facts should stay well away from anything to do with statistics.
Tony Lauck
"Diversity is the law of nature; no two entities in this universe are uniform." - P.R. Sarkar
Causation and explanation are the next steps in providing an understanding. I am mainly concerned with people dropping "statistical" and assuming that what they have found is meaningfully significant. You find this quite prevalently in research literature in the social sciences.
pounding of ones' chest ... I suggest you do a little searching and add if you locate as the addition would be a nice finishing touch to your post.
Everything matters, don't forget to tweak your placebos!
Not a problem, I was merely keeping it simple, not even addressing the larger issue; nor do I consider myself up to that task for that matter.
Everything matters, don't forget to tweak your placebos!
*
I understand.However it brings to mind an experience I had in 1st year university. I had this Economics professor who had a way of creating in his student's minds, mine included, a feeling of now understanding how it all works .
But in a class near the end of the semester he shocks us (well he certainly shocked me in any case) by declaring that everything we had learned was essentially incorrect, that as we progressed we'd discover it all to be egregious over-simplification. Yet he added that he still felt that his style of teaching with conviction , as he put it, was the correct way to approach a topic, basically a variation on the theme that one must first crawl to walk and that when at the crawl stage one should concentrate on doing that (alone) to the best of ones' abilities.
I was real life lesson that stuck with me.
Everything matters, don't forget to tweak your placebos!
Edits: 06/26/09
I would outline why each was said to be better. Then I would use data to show they were irrelevant. This is essentially teaching against the textbook.
Now I have written my own text and develop everything from the data themselves. It is still confusing, but for many I get them to think critically. The data show that "merit selection" of judges, where voters merely say whether a judge deserved another term or not does nothing for the quality of justice but does get younger judges and those with degrees from more prestigious law schools.
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