diff --git a/analysis.py b/analysis.py
index b3a7076..ff0cf9e 100644
--- a/analysis.py
+++ b/analysis.py
@@ -42,6 +42,19 @@ def main():
     pass
 
 
+def get_fit_info(folder):
+    fits_info = {}
+
+    for item in os.listdir(folder):
+        cell_folder = os.path.join(folder, item)
+
+        results = get_best_fit(cell_folder, use_comparable_error=True)
+        cell_behaviour, model_behaviour = results.get_behaviour_values()
+        fits_info[item] = [results.get_final_parameters(), model_behaviour, cell_behaviour]
+
+    return fits_info
+
+
 def calculate_percent_errors(fits_info):
     errors = {}
 
@@ -129,19 +142,6 @@ def parameter_correlations(fits_info):
     return labels, corr_values, corrected_p_values
 
 
-def get_fit_info(folder):
-    fits_info = {}
-
-    for item in os.listdir(folder):
-        cell_folder = os.path.join(folder, item)
-
-        results = get_best_fit(cell_folder)
-        cell_behaviour, model_behaviour = results.get_behaviour_values()
-        fits_info[item] = [results.get_final_parameters(), model_behaviour, cell_behaviour]
-
-    return fits_info
-
-
 def create_correlation_plot(labels, correlations, p_values):
 
     cleaned_cors = np.zeros(correlations.shape)
@@ -181,7 +181,8 @@ def create_correlation_plot(labels, correlations, p_values):
 def create_boxplots(errors):
 
     labels = ["{}_n:{}".format(k, len(errors[k])) for k in sorted(errors.keys())]
-
+    for k in sorted(errors.keys()):
+        print("{}: median %-error: {:.2f}".format(k, np.median(errors[k])))
     y_values = [errors[k] for k in sorted(errors.keys())]
 
     plt.boxplot(y_values)