- scipy.stats.mstats.chisquare(f_obs, f_exp=None)¶
Calculates a one-way chi square test.
The chi square test tests the null hypothesis that the categorical data has the given frequencies.
f_obs : array
observed frequencies in each category
f_exp : array, optional
expected frequencies in each category. By default the categories are assumed to be equally likely.
ddof : int, optional
adjustment to the degrees of freedom for the p-value
chisquare statistic : float
The chisquare test statistic
p : float
The p-value of the test.
This test is invalid when the observed or expected frequencies in each category are too small. A typical rule is that all of the observed and expected frequencies should be at least 5. The default degrees of freedom, k-1, are for the case when no parameters of the distribution are estimated. If p parameters are estimated by efficient maximum likelihood then the correct degrees of freedom are k-1-p. If the parameters are estimated in a different way, then the dof can be between k-1-p and k-1. However, it is also possible that the asymptotic distribution is not a chisquare, in which case this test is not appropriate.
[R210] Lowry, Richard. “Concepts and Applications of Inferential Statistics”. Chapter 8. http://faculty.vassar.edu/lowry/ch8pt1.html