diff --git a/Tets_Python/StartUpTest.py b/Tets_Python/StartUpTest.py
index 6151e8a..b624626 100644
--- a/Tets_Python/StartUpTest.py
+++ b/Tets_Python/StartUpTest.py
@@ -1,25 +1,30 @@
 from math import fabs as fabs
 from math import sqrt as sqrt
 from scipy.special import erfc as erfc
+import numpy as np
+from scipy import stats
 
 
 class StartUPTest:
 
     @staticmethod
-    def monobit_test(binary_data: str):
-
-        length_of_bit_string = len(binary_data)
+    def monobit_test(binary_data: bytes):
+        length_of_bit_string = len(binary_data) * 8
 
         # Variable for S(n)
         count = 0
-        # Iterate each bit in the string and compute for S(n)
-        for bit in binary_data:
-            if bit == 48:
-                # If bit is 0, then -1 from the S(n)
-                count -= 1
-            elif bit == 49:
-                # If bit is 1, then +1 to the S(n)
-                count += 1
+        # Iterate each byte in the string and compute for S(n)
+        for byte in binary_data:
+            # Iterate each bit in the byte
+            for i in range(8):
+                # Extract the i-th bit from the byte
+                bit = (byte >> i) & 1
+                if bit == 0:
+                    # If bit is 0, then -1 from the S(n)
+                    count -= 1
+                else:
+                    # If bit is 1, then +1 to the S(n)
+                    count += 1
 
         # Compute the test statistic
         sObs = count / sqrt(length_of_bit_string)
@@ -84,3 +89,14 @@ class StartUPTest:
         else:
             value = 0
         return value
+
+    @staticmethod
+    def chi_square(byte_data: bytes):
+        expected_probabilities = np.full(256, 1/256)  # Assuming 256 possible byte values
+        total_observations = len(byte_data)
+        observed_data, _ = np.histogram(list(byte_data), bins=np.arange(257))
+        expected_frequencies = expected_probabilities * total_observations
+
+        chi2_statistic, p_value = stats.chisquare(observed_data, f_exp=expected_frequencies)
+
+        return p_value, (p_value >= 0.01), chi2_statistic