diff --git a/bayesflow/approximators/continuous_approximator.py b/bayesflow/approximators/continuous_approximator.py
index a5dbf12a3..fb2e95a56 100644
--- a/bayesflow/approximators/continuous_approximator.py
+++ b/bayesflow/approximators/continuous_approximator.py
@@ -537,7 +537,7 @@ def _sample(
             )
             batch_shape = keras.ops.shape(inference_conditions)[:-1]
         else:
-            batch_shape = keras.ops.shape(inference_conditions)[1:-1]
+            batch_shape = (num_samples,)
 
         return self.inference_network.sample(
             batch_shape, conditions=inference_conditions, **filter_kwargs(kwargs, self.inference_network.sample)
diff --git a/tests/test_approximators/conftest.py b/tests/test_approximators/conftest.py
index 3c4d2fd4c..a56802a3e 100644
--- a/tests/test_approximators/conftest.py
+++ b/tests/test_approximators/conftest.py
@@ -68,6 +68,9 @@ def point_inference_network_with_multiple_parametric_scores():
 def point_approximator_with_single_parametric_score(adapter, point_inference_network, summary_network):
     from bayesflow import PointApproximator
 
+    if "-> 'inference_conditions'" not in str(adapter) and "-> 'summary_conditions'" not in str(adapter):
+        pytest.skip("point approximator does not support unconditional estimation")
+
     return PointApproximator(
         adapter=adapter,
         inference_network=point_inference_network,
@@ -81,6 +84,9 @@ def point_approximator_with_multiple_parametric_scores(
 ):
     from bayesflow import PointApproximator
 
+    if "-> 'inference_conditions'" not in str(adapter) and "-> 'summary_conditions'" not in str(adapter):
+        pytest.skip("point approximator does not support unconditional estimation")
+
     return PointApproximator(
         adapter=adapter,
         inference_network=point_inference_network_with_multiple_parametric_scores,
@@ -128,7 +134,16 @@ def adapter_with_sample_weight():
     )
 
 
-@pytest.fixture(params=["adapter_without_sample_weight", "adapter_with_sample_weight"])
+@pytest.fixture()
+def adapter_unconditional():
+    from bayesflow import ContinuousApproximator
+
+    return ContinuousApproximator.build_adapter(
+        inference_variables=["mean", "std"],
+    )
+
+
+@pytest.fixture(params=["adapter_unconditional", "adapter_without_sample_weight", "adapter_with_sample_weight"])
 def adapter(request):
     return request.getfixturevalue(request.param)