diff --git a/nncf/quantization/algorithms/weight_compression/algorithm.py b/nncf/quantization/algorithms/weight_compression/algorithm.py
index 81bb4406f0a..99728f6ef8e 100644
--- a/nncf/quantization/algorithms/weight_compression/algorithm.py
+++ b/nncf/quantization/algorithms/weight_compression/algorithm.py
@@ -498,7 +498,9 @@ def apply(
                 matmul_nodes_to_compress, graph
             )
             if statistic_points is None:
-                statistic_points = self.get_statistic_points(model, graph, matmul_input_to_output_nodes_map.keys())
+                statistic_points = self.get_statistic_points(
+                    model, graph, matmul_input_to_output_nodes_map.keys(), self._subset_size
+                )
                 statistic_points = self._collect_statistics(dataset, graph, model, statistic_points)
             statistics = self._get_statistics_for_weights_compression(
                 matmul_input_to_output_nodes_map, statistic_points
@@ -759,7 +761,6 @@ def get_statistic_points(
         model: TModel,
         graph: NNCFGraph,
         nodes_and_port_ids: Iterable[Tuple[NNCFNode, int]],
-        subset_size: Optional[int] = None,
     ) -> StatisticPointsContainer:
         """
         Returns statistic points, for which StatisticsCollector should collect statistics.
@@ -767,7 +768,6 @@ def get_statistic_points(
         :param model: Model for statistics collection.
         :param graph: Model graph.
         :param nodes_and_port_ids: Nodes and port ids for which statistics should be collected.
-        :param subset_size: Number of samples to collect.
         :return: Statistic points, for which StatisticsCollector should collect statistics.
         """
         statistic_container = StatisticPointsContainer()
@@ -781,7 +781,7 @@ def get_statistic_points(
                 # size dimension.
                 n_dims = len(graph.get_output_edges_by_port_id(node, output_port_id)[0].tensor_shape)
                 stat_collector = self._backend_entity.mean_statistic_collector(
-                    reduction_axes=tuple(range(n_dims - 1)), subset_size=subset_size
+                    reduction_axes=tuple(range(n_dims - 1)), subset_size=self._subset_size
                 )
                 statistic_container.add_statistic_point(
                     StatisticPoint(
diff --git a/tests/openvino/native/quantization/test_weights_compression.py b/tests/openvino/native/quantization/test_weights_compression.py
index db72b267698..ff52f115124 100644
--- a/tests/openvino/native/quantization/test_weights_compression.py
+++ b/tests/openvino/native/quantization/test_weights_compression.py
@@ -886,25 +886,34 @@ def test_compression_for_different_dtypes(activation_dtype, weight_dtype):
     check_compressed_matmul_subgraph(scale_multiply_node, activation_dtype, weight_dtype)
 
 
-DATASET_SIZE = 129
+DATASET_SIZE = 5
 
 
 @pytest.mark.parametrize(
-    ("subset_size", "ref_size"),
+    ("dataset_size", "subset_size", "ref_size"),
     (
-        (1, 1),
-        (5, 5),
-        (130, DATASET_SIZE),
+        (DATASET_SIZE, 1, 1),
+        (DATASET_SIZE, DATASET_SIZE, DATASET_SIZE),
+        (DATASET_SIZE, DATASET_SIZE + 1, DATASET_SIZE),
     ),
 )
-def test_valid_subset_size(mocker, subset_size, ref_size):
+@pytest.mark.parametrize(
+    ("compression_args", "multiplier_of_calls"),
+    (
+        (dict(mode=CompressWeightsMode.INT4_ASYM, ratio=1), 0),  # data-free, no reducers
+        (dict(mode=CompressWeightsMode.INT4_ASYM, ratio=0.5), 1),  # 1 reducer for mixed precision
+        (dict(mode=CompressWeightsMode.INT4_ASYM, ratio=1, awq=True), 2),  # mean & shape reducer for AWQ
+        (dict(mode=CompressWeightsMode.INT4_ASYM, ratio=0.5, awq=True), 3),  # 2 - for AWQ + 1 - for Mixed Precision
+    ),
+)
+def test_data_aware_all_layers(mocker, dataset_size, subset_size, ref_size, compression_args, multiplier_of_calls):
     model = IdentityMatmul().ov_model
-    dataset = Dataset([ACTIVATION] * DATASET_SIZE)
+    dataset = Dataset([ACTIVATION] * dataset_size)
     stats_spy = mocker.spy(AggregatorBase, "register_reduced_input")
 
-    compress_weights(model, mode=CompressWeightsMode.INT4_ASYM, ratio=0.5, dataset=dataset, subset_size=subset_size)
+    compress_weights(model, dataset=dataset, subset_size=subset_size, **compression_args)
 
-    assert stats_spy.call_count == ref_size
+    assert stats_spy.call_count == ref_size * multiplier_of_calls
 
 
 def test_default_subset_value():