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# scipy.stats.chisquare

`scipy.stats.``chisquare`(f_obs, f_exp=None, ddof=0)

  Calculates a one-way chi square test.

  The chi square test tests the null hypothesis that the categorical data has the given frequencies.

  Parameters : | 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
  Returns : | chisquare statistic : float The chisquare test statistic p : float The p-value of the test.

  Notes

  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 then the dof can be between k-1-p and k-1. However, it is also possible that the asymptotic distributions is not a chisquare, in which case this test is not appropriate.

  References

  [R77] | Lowry, Richard. “Concepts and Applications of Inferential Statistics”. Chapter 8. http://faculty.vassar.edu/lowry/ch8pt1.html

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