Author: Bob Durrett
Date: 18:08:27 01/23/04
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On January 23, 2004 at 15:24:31, Dann Corbit wrote: >30 experiments is a fairly standard rule as to when you should start to trust >the results for experimental data. > >From: >http://www.twoplustwo.com/mmessay8.html >"A good rule of thumb is to have at least 30 observations (playing sessions) for >the estimate to be reasonably accurate. However, the more the better, unless for >some reason you think the game for which you are trying to estimate your >standard deviation has changed significantly over some particular period of >time." > >From: >http://www.odu.edu/sci/xu/chapter3.pdf >"C. The Reliability of s as a Measure of Precision - the more measurements that >are made, the more reliable the value obtained for s. Usually 20 - 30 >measurements are necessary." > >From >http://www.stat.psu.edu/~resources/ClassNotes/ljs_21/ljs_21.PPT#11 >Concerning the central limit theorem, we have this: >Even if data are not normally distributed, as long as you take “large enough” >samples, the sample averages will at least be approximately normally >distributed. >Mean of sample averages is still mu >Standard error of sample averages is still sigma/sqrt(n). >In general, “large enough” means more than 30 measurements. > > >Of course, the more the merrier, when it comes to measurements. I don't wish to muddy the waters too much but the fact is that chess-playing programs or machines do not enter tournaments with zero information known about them. Just as in human tournaments, prior knowledge known prior to any games being played in the tournament can be very significant. Consider a trivial example: Suppose a top GM is to play a chess match against a true chess beginner. It is known apriori that the top GM is a whiz at chess and the beginner is a washout. Will it take thirty games to determine who is better? No, it will take ZERO games. The number of games required depends on the prior knowledge about the contestants. I hope this is not too distressful for anybody. : ) Bob D.
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