class_docstr: Results for rank comparison
  
  This is a subclass of HolderTuple that includes results from intermediate
  computations, as well as methods for hypothesis tests, confidence intervals
  and summary.
class_docstr_tok: ["Results","for","rank","comparison","This","is","a","subclass","of","HolderTuple","that","includes","results","from","intermediate","computations",",","as","well","as","methods","for","hypothesis","tests",",","confidence","intervals","and","summary","."]
docstr: Cohen's d, standardized mean difference under normality assumption.
  
  This computes the standardized mean difference, Cohen's d, effect size
  that is equivalent to the rank based probability ``p`` of being
  stochastically larger if we assume that the data is normally
  distributed, given by
  
      :math: `d = F^{-1}(p) * \sqrt{2}`
  
  where :math:`F^{-1}` is the inverse of the cdf of the normal
  distribution.
  
  Parameters
  ----------
  prob : float in (0, 1)
      Probability to be converted to Cohen's d effect size.
      If prob is None, then the ``prob1`` attribute is used.
  
  Returns
  -------
  equivalent Cohen's d effect size under normality assumption.
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code: def effectsize_normal(self, prob=None):
      """
      Cohen's d, standardized mean difference under normality assumption.
  
      This computes the standardized mean difference, Cohen's d, effect size
      that is equivalent to the rank based probability ``p`` of being
      stochastically larger if we assume that the data is normally
      distributed, given by
  
          :math: `d = F^{-1}(p) * \\sqrt{2}`
  
      where :math:`F^{-1}` is the inverse of the cdf of the normal
      distribution.
  
      Parameters
      ----------
      prob : float in (0, 1)
          Probability to be converted to Cohen's d effect size.
          If prob is None, then the ``prob1`` attribute is used.
  
      Returns
      -------
      equivalent Cohen's d effect size under normality assumption.
  
      """
      if prob is None:
          prob = self.prob1
      return stats.norm.ppf(prob) * np.sqrt(2)
  
file_path: statsmodels/statsmodels/stats/nonparametric.py
imports: import numpy
  from scipy import stats
  from scipy.stats import rankdata
  from statsmodels.stats.base import HolderTuple
  from statsmodels.stats.weightstats import _tconfint_generic, _tstat_generic, _zconfint_generic, _zstat_generic
label_desc: Use image node_id 5 for calling the RankCompareResult obj's underlying member method code with example usage: obj.effectsize_normal(prob) and returns: unknown