diff --git a/tests/sweep_framework/sweeps/normalization/batch_norm/batch_norm.py b/tests/sweep_framework/sweeps/normalization/batch_norm/batch_norm.py
index 4483c385fff..3079d177577 100644
--- a/tests/sweep_framework/sweeps/normalization/batch_norm/batch_norm.py
+++ b/tests/sweep_framework/sweeps/normalization/batch_norm/batch_norm.py
@@ -7,7 +7,9 @@
 
 import torch
 import random
+import itertools
 import ttnn
+import pytest
 from tests.sweep_framework.sweep_utils.utils import gen_shapes
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
@@ -37,7 +39,7 @@
 }
 
 
-def run(
+def run_batch_norm(
     input_shape,
     input_dtype,
     input_layout,
@@ -49,9 +51,8 @@ def run(
     bias,
     eps,
     momentum,
-    *,
     device,
-) -> list:
+):
     data_seed = random.randint(0, 20000000)
     torch.manual_seed(data_seed)
 
@@ -110,14 +111,6 @@ def run(
     output_tensor = ttnn.to_torch(result)
     e2e_perf = stop_measuring_time(start_time)
 
-    tt_updated_mean = None
-    tt_updated_var = None
-    if training:
-        if check_mean:
-            tt_updated_mean = ttnn.to_torch(mean_tensor)
-        if check_var:
-            tt_updated_var = ttnn.to_torch(var_tensor)
-
     torch_result = torch.nn.functional.batch_norm(
         input=in_data,
         running_mean=mean_data,
@@ -129,4 +122,97 @@ def run(
         momentum=momentum,
     )
 
-    return [check_with_pcc(torch_result, output_tensor, 0.99), e2e_perf]
+    passed = []
+    output_string = ""
+    passed_, output_string_ = check_with_pcc(torch_result, output_tensor, 0.99)
+    passed.append(passed_)
+    output_string += output_string_ + ", "
+
+    if training:
+        channels = input_shape[1]
+        if check_mean:
+            tt_updated_mean = ttnn.to_torch(mean_tensor)
+            passed_, output_string_ = check_with_pcc(tt_updated_mean, mean_data.view(1, channels, 1, 1), 0.99)
+            passed.append(passed_)
+            output_string += output_string_ + ", "
+        if check_var:
+            tt_updated_var = ttnn.to_torch(var_tensor)
+            passed_, output_string_ = check_with_pcc(tt_updated_var, var_data.view(1, channels, 1, 1), 0.99)
+            passed.append(passed_)
+            output_string += output_string_ + ", "
+
+    if all(passed):
+        passed = True
+    else:
+        passed = False
+
+    output_string = output_string[:-2]
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [(passed, output_string), e2e_perf]
+
+
+def run(
+    input_shape,
+    input_dtype,
+    input_layout,
+    input_memory_config,
+    training,
+    check_mean,
+    check_var,
+    weight,
+    bias,
+    eps,
+    momentum,
+    *,
+    device,
+) -> list:
+    return run_batch_norm(
+        input_shape,
+        input_dtype,
+        input_layout,
+        input_memory_config,
+        training,
+        check_mean,
+        check_var,
+        weight,
+        bias,
+        eps,
+        momentum,
+        device,
+    )
+
+
+param_keys = parameters["BN_Testing"].keys()
+param_values = itertools.product(*parameters["BN_Testing"].values())
+
+
+@pytest.mark.parametrize(",".join(param_keys), list(param_values))
+def test_batch_norm(
+    input_shape,
+    input_dtype,
+    input_layout,
+    input_memory_config,
+    training,
+    check_mean,
+    check_var,
+    weight,
+    bias,
+    eps,
+    momentum,
+    device,
+):
+    run_batch_norm(
+        input_shape,
+        input_dtype,
+        input_layout,
+        input_memory_config,
+        training,
+        check_mean,
+        check_var,
+        weight,
+        bias,
+        eps,
+        momentum,
+        device,
+    )