docstr: In this case, parameters a and b aren't used, since this information is taken
  directly from the corpus categories.
  
  Returns
  -------
docstr_tok: ["In","this","case",",","parameters","a","and","b","are","n't","used",",","since","this","information","is","taken","directly","from","the","corpus","categories",".","Returns","--","--","--","-"]
code: 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
  
imports: from abc import ABCMeta, abstractmethod
  from typing import Tuple
  import numpy
  import pandas
  from scattertext.ParsedCorpus import ParsedCorpus
  from scipy import stats
  from scipy.sparse import vstack
  from scattertext.util import inherits_from
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scattertext.termranking.TermRanker import TermRanker
label_desc: Use image node_id 15 for calling the CorpusBasedTermScorer obj's underlying member method code with example usage: obj.get_t_statistics() and returns: tt,  pvals