A question that seems to be on a lot of people's minds is, "Are VP machine fair and random?"
I answered this particular question to my own satisfaction in the mid 90's when I had access to the enormous sample of hands that being a team manager afforded me. I do not expect anyone to believe me or take it on faith, and I'm a strong supporter of skepticism and personal discovery, so...
It seems like every time someone, anyone, makes a comment about machines not being random or merely shares their beliefs and observations in forum, someone else (myself included) chimes in and points out that their math is flawed, their sample is too small, or their conclusion are wrong. What almost never seems to get discussed is what would be correct math, a large enough sample and a proper method for reaching a good conclusion.
No one is going to be able to do it the way I did, so how then???
I'd like to start this thread specifically for the purposes of discussing what it would take to prove that machines are not fair and random or visa versa as the case may be:
1. What should one record and how?
2. What is a large enough sample? (And how to parse the sample.)
3. What math should they use to dismiss chance as the cause of their results?
4. And perhaps most importantly how do they make the information meet the standard of the scientific method, with peer review and replication of results???
If #4 is not met any study done will only succeed in convincing the person doing the study.
What's been offered so far hasn't convinced anyone that wasn't already convinced and isn't likely to start convincing people anytime soon. What we need is a simple detailed template of the steps required that anyone can follow and prove it to themselves, one way or the other.
I can think of no better way to spend our time on a VP forum than working on this problem and resolving it once and for all.
And please note: There should be no need for argument in this thread about whether or not VP machines are fair and random. That would be a conclusion. This thread is not about conclusions, it's about how to formulate them.
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I've always found Bayesian Inference goes best with a nice Chianti or Barbera...of course I'm always adjusting that based on new information. ~FK
