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Re: st: fishers exact test
No problem. It is saying that all tables that satisfy
these marginal constraints are as likely or less
likely than the table you have observed, within
round-off. Round-off also explains the
A better name than exact, since it rarely is, would
be something like permutation.
I have a 2x2 table:
first row: 41 12
second row: 6 1
Running tabi gives a p-value for Fisher's exact test of 1.0 (two sided) and
0.52 (one sided). The hypothesis suggests using two sided value.
A reviewer commented that p-values, unless the data is quite unique, should
not be 1.0
Why is the p-value for Fisher's exact test exactly 1.0? Does this make
Moreover, after viewing "return list," the p-value isn't 1.0 but
1.00000000012. How can a p-value exceed 1.0?
Also, looking on the web, some sites calculate the following:
Two sided p-values for p(O>=E|O<=E)
p-value= 1.0000000000* (the sum of small p's)
Two sided p-value for p(O>E|O<E)
p-value= 0.6385656450 (the sum of small p's)
Should I be using a different test?
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