Tuesday, February 28, 2006

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 1.00000000012.

A better name than exact, since it rarely is, would be something like permutation.


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 > > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/


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