diff --git a/delta_measure.py b/delta_measure.py
index 4d2025d17eab82638fca47974432cea877a1eb80..84bb84a2676df12806801bb9bf5ae3652514b710 100644
--- a/delta_measure.py
+++ b/delta_measure.py
@@ -5,6 +5,7 @@ import pandas as pd
 import statistics
 import re
 import dataframe_image as dfi
+import scipy.stats
 
 data_overview = pd.DataFrame(pd.read_csv("data_overview/data_overview.csv", index_col=0))
 
@@ -27,6 +28,9 @@ mean_std_dev_list = [[columnName, columnData.mean(), columnData.std()] for colum
 # Create a new DataFrame with the same column names and index labels as data_overview
 z_scores_all_data = pd.DataFrame(columns=data_overview.columns, index=data_overview.index)
 
+p_values_all_data = pd.DataFrame(columns=data_overview.columns, index=data_overview.index)
+
+
 # Iterate over each cell in the data_overview DataFrame and write the corresponding z-score in the z_scores_all_data DataFrame
 for index, row in data_overview.iterrows():
     for column in data_overview.columns:
@@ -34,8 +38,12 @@ for index, row in data_overview.iterrows():
         cell_value = data_overview.loc[index, column]
         z_score = (cell_value - mean) / std_dev
         z_scores_all_data.loc[index, column] = z_score
+        p_value = scipy.stats.norm.sf(abs(z_score))
+        p_values_all_data[index, column] = p_value
+
 
 dfi.export(z_scores_all_data, "data_overview/z_scores_all_data.png", table_conversion = "matplotlib")
+dfi.export(p_values_all_data, "data_overview/p_values_all_data.png", table_conversion = "matplotlib")
 
 print(z_scores_all_data)