diff --git a/onnxruntime/python/tools/quantization/qdq_quantizer.py b/onnxruntime/python/tools/quantization/qdq_quantizer.py
index 6895276e12..e475c4d033 100644
--- a/onnxruntime/python/tools/quantization/qdq_quantizer.py
+++ b/onnxruntime/python/tools/quantization/qdq_quantizer.py
@@ -86,8 +86,8 @@ class QDQQuantizer(ONNXQuantizer):
         # So, we don't recommend to add QDQ to node's output under such condition.
         self.op_types_to_exclude_output_quantization = (
             []
-            if "OpTypesToExcludeOutputQuantizatioin" not in extra_options
-            else extra_options["OpTypesToExcludeOutputQuantizatioin"]
+            if "OpTypesToExcludeOutputQuantization" not in extra_options
+            else extra_options["OpTypesToExcludeOutputQuantization"]
         )
 
         # We do quantization on Dequantizelinear's input to remove Quantizelinear for weight as an optimization.
diff --git a/onnxruntime/python/tools/quantization/quantize.py b/onnxruntime/python/tools/quantization/quantize.py
index 4aa28d54de..80ae592d49 100644
--- a/onnxruntime/python/tools/quantization/quantize.py
+++ b/onnxruntime/python/tools/quantization/quantize.py
@@ -118,7 +118,7 @@ class StaticQuantConfig(QuantConfig):
                         Default is False which quantizes floating-point weight and feeds it to solely inserted
                         DeQuantizeLinear node. If True, it remains floating-point weight and inserts both
                         QuantizeLinear/DeQuantizeLinear nodes to weight.
-                    OpTypesToExcludeOutputQuantizatioin = list of op type :
+                    OpTypesToExcludeOutputQuantization = list of op type :
                         Default is []. If any op type is specified, it won't quantize the output of ops with this
                         specific op types.
                     DedicatedQDQPair = True/False :
@@ -304,7 +304,7 @@ def quantize_static(
                     Default is False which quantizes floating-point weight and feeds it to solely inserted
                     DeQuantizeLinear node. If True, it remains floating-point weight and inserts both
                     QuantizeLinear/DeQuantizeLinear nodes to weight.
-                OpTypesToExcludeOutputQuantizatioin = list of op type :
+                OpTypesToExcludeOutputQuantization = list of op type :
                     Default is []. If any op type is specified, it won't quantize the output of ops with this
                     specific op types.
                 DedicatedQDQPair = True/False :
diff --git a/onnxruntime/test/python/quantization/test_qdq.py b/onnxruntime/test/python/quantization/test_qdq.py
index 35f6192ff6..c50fb9870b 100644
--- a/onnxruntime/test/python/quantization/test_qdq.py
+++ b/onnxruntime/test/python/quantization/test_qdq.py
@@ -101,7 +101,7 @@ class TestQDQExtraOptions(unittest.TestCase):
             {
                 "ActivationSymmetric": True,
                 "AddQDQPairToWeight": True,
-                "OpTypesToExcludeOutputQuantizatioin": [],
+                "OpTypesToExcludeOutputQuantization": [],
             },
         )  # extra_options
         quantizer.quantize_model()
@@ -202,7 +202,7 @@ class TestQDQExtraOptions(unittest.TestCase):
             {
                 "ActivationSymmetric": True,
                 "AddQDQPairToWeight": True,
-                "OpTypesToExcludeOutputQuantizatioin": op_types_to_quantize,
+                "OpTypesToExcludeOutputQuantization": op_types_to_quantize,
                 "DedicatedQDQPair": True,
             },
         )  # extra_options