diff --git a/tests/conftest.py b/tests/conftest.py
index 7c89c5d68a0..3a9986ad4c6 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -39,6 +39,12 @@ class DummyDevice(DefaultGaussian):
     _operation_map["Kerr"] = lambda *x, **y: np.identity(2)
 
 
+@pytest.fixture(autouse=True)
+def set_numpy_seed():
+    np.random.seed(9872653)
+    yield
+
+
 @pytest.fixture(scope="session")
 def tol():
     """Numerical tolerance for equality tests."""
diff --git a/tests/gradients/finite_diff/test_finite_difference_shot_vec.py b/tests/gradients/finite_diff/test_finite_difference_shot_vec.py
index 641d9138b7c..0f1b19609fb 100644
--- a/tests/gradients/finite_diff/test_finite_difference_shot_vec.py
+++ b/tests/gradients/finite_diff/test_finite_difference_shot_vec.py
@@ -718,7 +718,7 @@ def test_multiple_expectation_values(self, approx_order, strategy, validate):
 
             assert isinstance(res[0], tuple)
             assert len(res[0]) == 2
-            assert np.allclose(res[0], [-np.sin(x), 0], atol=0.1, rtol=0)
+            assert np.allclose(res[0], [-np.sin(x), 0], atol=0.15, rtol=0)
             assert isinstance(res[0][0], numpy.ndarray)
             assert isinstance(res[0][1], numpy.ndarray)
 
diff --git a/tests/gradients/parameter_shift/test_parameter_shift_shot_vec.py b/tests/gradients/parameter_shift/test_parameter_shift_shot_vec.py
index 85463888985..0d2d97c596b 100644
--- a/tests/gradients/parameter_shift/test_parameter_shift_shot_vec.py
+++ b/tests/gradients/parameter_shift/test_parameter_shift_shot_vec.py
@@ -1305,7 +1305,7 @@ def test_non_involutory_variance_single_param(self):
         for gradF in all_gradF:
             assert isinstance(gradF, np.ndarray)
             assert gradF.shape == ()
-            assert gradF == pytest.approx(expected, abs=1)
+            assert qml.math.allclose(gradF, expected, atol=2 * _herm_shot_vec_tol)
 
     @flaky(max_runs=5)
     def test_non_involutory_variance_multi_param(self):
@@ -1359,7 +1359,7 @@ def test_non_involutory_variance_multi_param(self):
             # Note: the tolerances here are significantly higher than in usual tests
             # due to the stochasticity of the test case
             assert gradF[0] == pytest.approx(expected, abs=2)
-            assert gradF[1] == pytest.approx(expected, abs=1)
+            assert qml.math.allclose(gradF[1], expected, atol=1.5)
 
     @flaky(max_runs=8)
     def test_involutory_and_noninvolutory_variance_single_param(self):
@@ -1486,8 +1486,8 @@ def test_involutory_and_noninvolutory_variance_multi_param(self):
 
             assert np.allclose(shot_vec_result[0][0], expected[0], atol=1)
             assert np.allclose(shot_vec_result[0][1], expected[1], atol=1)
-            assert np.allclose(shot_vec_result[1][0], expected[2], atol=1)
-            assert np.allclose(shot_vec_result[1][1], expected[3], atol=1)
+            assert np.allclose(shot_vec_result[1][0], expected[2], atol=1.5)
+            assert np.allclose(shot_vec_result[1][1], expected[3], atol=1.5)
 
     @pytest.mark.parametrize("ind", [0, 1])
     def test_var_and_probs_single_param(self, ind):
diff --git a/tests/transforms/test_hamiltonian_expand.py b/tests/transforms/test_hamiltonian_expand.py
index 6adad868778..578430998d6 100644
--- a/tests/transforms/test_hamiltonian_expand.py
+++ b/tests/transforms/test_hamiltonian_expand.py
@@ -345,9 +345,7 @@ def test_processing_function_shot_vectors_broadcasting(self, H, expected, groupi
         """Tests that the processing function works with shot vectors, parameter broadcasting,
         and grouping with different number of coefficients in each group"""
 
-        np.random.seed(824)
-        dev_with_shot_vector = qml.device("default.qubit", shots=[(8000, 4)])
-
+        dev_with_shot_vector = qml.device("default.qubit", shots=[(10000, 4)])
         if grouping:
             H.compute_grouping()
 
diff --git a/tests/transforms/test_qcut.py b/tests/transforms/test_qcut.py
index 7225ea4bada..0ab9d64d978 100644
--- a/tests/transforms/test_qcut.py
+++ b/tests/transforms/test_qcut.py
@@ -2506,7 +2506,7 @@ def target_circuit(v):
             qml.RX(2.3, wires=2)
             return qml.expval(qml.PauliZ(wires=0) @ qml.PauliZ(wires=2))
 
-        dev = dev_fn(wires=2, shots=10000)
+        dev = dev_fn(wires=2, shots=20000)
 
         @partial(qml.cut_circuit_mc, classical_processing_fn=fn)
         @qml.qnode(dev)