The Chisquare test is based upon a normal approx of the (essentially) binomial
distribution for the data in question. Small EXPECTED (not observed) values
(<5) suggest a asymetric distribution and potential errors in inferential
conclusions. The alternative is the exact test, which calculates the exact
probabilities of the observed distribution, or a more extreme one, given the
constraining expectations.
It is usually much more useful to make the statistics fit the data question than
to assume or force the vice versa.
--Bob Porter, Tampa
> Hi,
> In the chisq.test(), if the expected frequency for some categories is
<5,
there will be a warning message which says> Warning message:
> Chi-squared approximation may be incorrect in: chisq.test(x, p = probs)
>
> I am wondering whether there are some methods to get rid of this mistake...
Seems the ?chisq.test() doesn't provide more> options to solve this problem. Or, the only choice is to preprocess the
data
to avoid this situation?>
> Thanks a lot!
>
> aprilsun
