def get_t_statistics(self):
        '''
        In this case, parameters a and b aren't used, since this information is taken
        directly from the corpus categories.

        Returns
        -------

        '''

        X = self._get_X()
        cat_X, ncat_X = self._get_cat_and_ncat(X)

        mean_delta = self._get_mean_delta(cat_X, ncat_X)
        cat_var = sparse_var(cat_X)
        ncat_var = sparse_var(ncat_X)
        cat_n = cat_X.shape[0]
        ncat_n = ncat_X.shape[0]
        pooled_stderr = np.sqrt(cat_var / cat_n + ncat_var / ncat_n)

        tt = mean_delta / pooled_stderr

        # Use Satterthaite-Welch adjustment for degrees of freedom
        degs_of_freedom = (cat_var ** 2 / cat_n + ncat_var ** 2 / ncat_n) ** 2 / (
                (cat_var ** 2 / cat_n) ** 2 / (cat_n - 1)
                + (ncat_var ** 2 / ncat_n) ** 2 / (ncat_n - 1)
        )

        only_in_neutral_mask = self.tdf_[['cat', 'ncat']].sum(axis=1) == 0
        pvals = stats.t.sf(np.abs(tt), degs_of_freedom)
        tt[only_in_neutral_mask] = 0
        pvals[only_in_neutral_mask] = 0

        return tt, pvals

        In this case, parameters a and b aren't used, since this information is taken
        directly from the corpus categories.

        Returns
        -------

        
scattertext/termscoring/CorpusBasedTermScorer.py