Your best guess before hand effects the Bayesian inference:
http://en.wikipedia.org/wiki/Bayesian_inference
Basically, you're using a test which always has a chance of false negative and false positive. Bayesian inference is a technique to adjust your previous assumption to the results of your data knowing that there is a certain false report rate. For example, you might assume that the ER of a certain machine is 100.76% with perfect play. After taking data, you can make a Bayesian inference on what the actual ER is, which will include your play error rate.
--- In vpFREE@yahoogroups.com, "johnnyzee48127" <greeklandjohnny@...> wrote:
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> --- In vpFREE@yahoogroups.com, Mitchell Tsai <tsai@> wrote:
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> > > If the sample is x standard deviations from expected, what
> > > chance of a gaffed machine does that translate to?
> > >
> >
> > This will depend on
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> > (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.
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> 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?
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