diff --git a/.github/workflows/style.yaml b/.github/workflows/style.yaml
index a451ac89d..3c2da4421 100644
--- a/.github/workflows/style.yaml
+++ b/.github/workflows/style.yaml
@@ -6,12 +6,10 @@ on:
     branches:
       - main
       - dev
-      - update-workflows
   push:
     branches:
       - main
       - dev
-      - update-workflows
 
 jobs:
   check-code-style:
diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml
index f90389f69..ab3d03078 100644
--- a/.github/workflows/tests.yaml
+++ b/.github/workflows/tests.yaml
@@ -24,7 +24,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10"]  # we usually only need to test the oldest python version
         backend: ["jax", "tensorflow", "torch"]
 
     runs-on: ${{ matrix.os }}
diff --git a/tests/conftest.py b/tests/conftest.py
index 6e1e69db1..560b7c59b 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -41,7 +41,7 @@ def pytest_make_parametrize_id(config, val, argname):
     return f"{argname}={repr(val)}"
 
 
-@pytest.fixture(params=[2, 3], scope="session")
+@pytest.fixture(params=[2], scope="session")
 def batch_size(request):
     return request.param
 
@@ -94,33 +94,3 @@ def random_set(batch_size, set_size, feature_size):
 @pytest.fixture(params=[2, 3], scope="session")
 def set_size(request):
     return request.param
-
-
-@pytest.fixture(params=["two_moons"], scope="session")
-def simulator(request):
-    return request.getfixturevalue(request.param)
-
-
-@pytest.fixture(scope="session")
-def training_dataset(simulator, batch_size):
-    from bayesflow.datasets import OfflineDataset
-
-    num_batches = 128
-    samples = simulator.sample((num_batches * batch_size,))
-    return OfflineDataset(samples, batch_size=batch_size)
-
-
-@pytest.fixture(scope="session")
-def two_moons(batch_size):
-    from bayesflow.simulators import TwoMoonsSimulator
-
-    return TwoMoonsSimulator()
-
-
-@pytest.fixture(scope="session")
-def validation_dataset(simulator, batch_size):
-    from bayesflow.datasets import OfflineDataset
-
-    num_batches = 16
-    samples = simulator.sample((num_batches * batch_size,))
-    return OfflineDataset(samples, batch_size=batch_size)
diff --git a/tests/test_approximators/test_point_approximators/__init__.py b/tests/test_approximators/test_point_approximators/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/test_approximators/test_point_approximators/conftest.py b/tests/test_approximators/test_point_approximators/conftest.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/test_distributions/conftest.py b/tests/test_distributions/conftest.py
index 29c5b4139..e06ed18af 100644
--- a/tests/test_distributions/conftest.py
+++ b/tests/test_distributions/conftest.py
@@ -3,7 +3,7 @@
 import keras
 
 
-@pytest.fixture(params=[2, 3])
+@pytest.fixture(params=[2])
 def batch_size(request):
     return request.param
 
diff --git a/tests/test_links/conftest.py b/tests/test_links/conftest.py
index 53e9eeac8..be7730ef2 100644
--- a/tests/test_links/conftest.py
+++ b/tests/test_links/conftest.py
@@ -82,8 +82,3 @@ def quantiles(request):
 @pytest.fixture()
 def unordered(batch_size, num_quantiles, num_variables):
     return keras.random.normal((batch_size, num_quantiles, num_variables))
-
-
-# @pytest.fixture()
-# def random_matrix_batch(batch_size, num_variables):
-#     return keras.random.normal((batch_size, num_variables, num_variables))
diff --git a/tests/test_networks/test_summary_networks.py b/tests/test_networks/test_summary_networks.py
index 50e1726c1..74ce1f5fd 100644
--- a/tests/test_networks/test_summary_networks.py
+++ b/tests/test_networks/test_summary_networks.py
@@ -103,7 +103,7 @@ def test_save_and_load(tmp_path, summary_network, random_set):
 @pytest.mark.parametrize("stage", ["training", "validation"])
 def test_compute_metrics(stage, summary_network, random_set):
     if summary_network is None:
-        pytest.skip()
+        pytest.skip("Nothing to do, because there is no summary network.")
 
     summary_network.build(keras.ops.shape(random_set))
 
diff --git a/tests/utils/check_combinations.py b/tests/utils/check_combinations.py
index 8d3fa5d46..8565703c8 100644
--- a/tests/utils/check_combinations.py
+++ b/tests/utils/check_combinations.py
@@ -13,12 +13,12 @@ def check_combination_simulator_adapter(simulator, adapter):
         with pytest.raises(KeyError):
             adapter(simulator.sample(1))
         # Don't use this fixture combination for further tests.
-        pytest.skip()
+        pytest.skip(reason="Do not use this fixture combination for further tests")  # TODO: better reason
     elif simulator_with_sample_weight and not adapter_with_sample_weight:
         # When a weight key is present, but the adapter does not configure it
         # to be used as sample weight, no error is raised currently.
         # Don't use this fixture combination for further tests.
-        pytest.skip()
+        pytest.skip(reason="Do not use this fixture combination for further tests")  # TODO: better reason
 
 
 def check_approximator_multivariate_normal_score(approximator):
@@ -28,4 +28,4 @@ def check_approximator_multivariate_normal_score(approximator):
     if isinstance(approximator, PointApproximator):
         for score in approximator.inference_network.scores.values():
             if isinstance(score, MultivariateNormalScore):
-                pytest.skip()
+                pytest.skip(reason="MultivariateNormalScore is unstable")