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
1.00000000012.
A better name than exact, since it rarely is, would
be something like permutation.
m.p.
Richard_Lenhardt@rush.edu wrote:

Hello,

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

sense?

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?

Thank you,

Richard Lenhardt

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