Bruce Weaver <[EMAIL PROTECTED]> wrote in message news:<[EMAIL PROTECTED]>... > Perhaps because the way the problem was described, persons A > and B are flipping the *same* coin, not two different coins?
I understood the circumstance. That might affect the probability of success, but I don't see how to introduce much dependence. Could you describe a way in which you believe that B's 3rd flip could be dependent on A's 3rd flip? A's 5 flip? B's first flip? I can see only two kinds of dependence. One would be extremely small (the coin wears as you use it, so p may slowly change over time, which introduces a dependence of a kind). The other could be less small, but could be dramatically reduced by appropriate throwing protocols (as Persi Diaconis and others have shown, each coin flip isn't absolutely independent of the face that is uppermost when you flip it, so if you always pick up the coin and place it for the next flip in the same way, so the face that's uppermost when you flip it next time is perfectly dependent on the face that was uppermost when it landed last time, there is a potential for a bit of serial dependence). Both these effects will be small relative to the noise in your estimates unless the number of tosses is very, very large indeed. Neither of these effects really come into what most people mean when they talk of "dependent samples", where they'd posit some kind of relationship between A's i-th toss and B's i-th toss. Glen . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
