3.11.9.2.1. statsmodels.stats.gof.chisquare

statsmodels.stats.gof.chisquare(f_obs, f_exp=None, value=0, ddof=0, return_basic=True)

chisquare goodness-of-fit test

The null hypothesis is that the distance between the expected distribution and the observed frequencies is value. The alternative hypothesis is that the distance is larger than value. value is normalized in terms of effect size.

The standard chisquare test has the null hypothesis that value=0, that is the distributions are the same.

See also

powerdiscrepancy, scipy.stats.chisquare

Notes

The case with value greater than zero is similar to an equivalence test, that the exact null hypothesis is replaced by an approximate hypothesis. However, TOST “reverses” null and alternative hypothesis, while here the alternative hypothesis is that the distance (divergence) is larger than a threshold.

References

McLaren, ... Drost,...