code: def cohensd2problarger(d):
      """
      Convert Cohen's d effect size to stochastically-larger-probability.
  
      This assumes observations are normally distributed.
  
      Computed as
  
          p = Prob(x1 > x2) = F(d / sqrt(2))
  
      where `F` is cdf of normal distribution. Cohen's d is defined as
  
          d = (mean1 - mean2) / std
  
      where ``std`` is the pooled within standard deviation.
  
      Parameters
      ----------
      d : float or array_like
          Cohen's d effect size for difference mean1 - mean2.
  
      Returns
      -------
      prob : float or ndarray
          Prob(x1 > x2)
      """
  
      return stats.norm.cdf(d / 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 a global function with example usage: cohensd2problarger(d) and returns: stats