--- In vpFREE@yahoogroups.com, Mitchell Tsai <tsai@...> wrote:
>
> > If the sample is x standard deviations from expected, what
> > chance of a gaffed machine does that translate to?
> >
>
> This will depend on
>
> (1) before any data, how likely do you believe the machine is biased e.g. Before the test, you thought there was a 1% change of a gaffed machine The test tells you afterwards that there is a 30-40% chance of a gaffed machine.
I'm not sure a person's opinion of the game ( what percent chance machine is non-fair) is all that important. If I think a machine has a 50% chance of being non-fair vs having a 30% chance of being non fair, how does that affect my experiment?
The whole idea of my post is to show that making an accurate conclusion based on your play is a lot more work than is commonly believed.
" I had no quads in 3 hours of play" is an almost worthless statement. Once a person has a hypothesis to test, this group can look at sample size, appropriate statistic, noise factors,e tc and see if the planned experiment will actually give you the answer you need.
Let's say I think quads are being shorted on a single line machine. My hypothesis is "Quads appear less frequently than expected". I will play 5000 hands of JOB and record my results. If my results are less than 10% likely to occur, I will repeat. If they are less than 10% likely again, I will conclude this machine is non-fair. We can go to chi squared type testing or confidence interval type testing and see what is the chance that I run 2 trials and both trials show a quad frequency in the bottom 10% of possibilities.
The main point is to be very, very detail oriented on both what you are trying to prove (or disprove) and rigorous in the data collection. Otherwise, you can't really make an intelligent conclusion.
Did Frank ever publish his program?
