text: def compare_samples(var_0_list, var_1_list):
  
      print(f"{len(var_0_list)} samples loaded into variable 0")
      print(f"{len(var_1_list)} samples loaded into variable 1")
      print("------------------------------------")
  
      # Begin F Test
      print("Beginning F Test")
      print("------------------------------------")
      mean_var_0 = np.mean(var_0_list)
      mean_var_1 = np.mean(var_1_list)
      print(f"mean var 0: {mean_var_0}")
      print(f"mean var 1: {mean_var_1}")
      f = np.var(var_0_list)/np.var(var_1_list)
      print(f"F: {f}")
      f_crit = sps.f.cdf(f, len(var_0_list) - 1, len(var_1_list) - 1)
      print(f"F critical: {f_crit}")
      print("------------------------------------")
  
      # Are variances equal?
      print("Are variances equal?")
      print("------------------------------------")
      print("is mean(var 0) > mean(var 2) and F < F critical?")
      print("or is mean(var 0) < mean(var 2) and F > F critical? ")
      print("------------------------------------")
      var_equal = (mean_var_0 > mean_var_1) and (f < f_crit)
      var_equal = var_equal or ((mean_var_0 < mean_var_1) and (f > f_crit))
      if var_equal:
          print("Variances are Equal")
      else:
          print("Variances are Unequal")
      print("------------------------------------")
  
      if var_equal:
          print("Begin two-tailed two-sample t-test assuming equal variances")
      else:
          print("Begin two-tailed two-sample t-test assuming unequal variances")
      t_t_test, p_t_test = sps.ttest_ind(var_0_list, var_1_list, equal_var=var_equal)
      print(f"T-Test Value: {t_t_test}")
      print(f"Two tailed p: {p_t_test}")
      print("------------------------------------")
      print("Assuming alpha=0.05")
      print("------------------------------------")
      if p_t_test > 0.05:
          print("Null Hypothesis Accepted, Algorithm is NOT statistically better")
      else:
          print("Null Hypothesis Rejected, Algorithm is statistically better")