diff --git a/load_dataset.py b/load_dataset.py
index be214e4..1a4d9e2 100644
--- a/load_dataset.py
+++ b/load_dataset.py
@@ -79,7 +79,7 @@ def analisis_univariado(dfi, target=None, continuas=[], discretas=[]):
 
             resultsmody.append([
                 var, " ", f"{median1:.1f} ({ql1:.1f} - {qr1:.1f})", f"{median1:.1f} ({ql2:.1f} - {qr2:.1f})",
-                f"{t_pval:.3f}", ("*" if mw_pval < 0.05 else "NS"), f"{mediang:.1f} ({qlg:.1f} - {qrg:.1f})"
+                f"{mw_pval:.3f}", ("*" if mw_pval < 0.05 else "NS"), f"{mediang:.1f} ({qlg:.1f} - {qrg:.1f})"
             ])
 
     # Análisis de variables discretas
@@ -107,7 +107,7 @@ def analisis_univariado(dfi, target=None, continuas=[], discretas=[]):
         totf = 100 * tot / len(dfi[var])
         resultsmody.append([
             var, " ", f"{percentages[0][1]:.1f} ({freq_table[0][1]}/{len(group1)})", f"{percentages[1][1]:.1f} ({freq_table[1][1]}/{len(group2)})"
-            f"{t_pval:.3f}", ("*" if fisher_pval < 0.05 else "NS"), f"{totf:.1f} ({tot}/{len(dfi[var])})"
+            f"{fisher_pval:.3f}", ("*" if fisher_pval < 0.05 else "NS"), f"{totf:.1f} ({tot}/{len(dfi[var])})"
         ])
 
     # Crear DataFrame con los resultados