diff --git a/pyerrors/fits.py b/pyerrors/fits.py
index 8e57f696..13b70f7f 100644
--- a/pyerrors/fits.py
+++ b/pyerrors/fits.py
@@ -18,8 +18,6 @@ from odrpack import odr_fit
 
 from .obs import Obs, cov_Obs, covariance, derived_observable, invert_corr_cov_cholesky
 
-_rng = np.random.default_rng()
-
 
 class Fit_result(Sequence):
     """Represents fit results.
@@ -948,6 +946,6 @@ def _construct_prior_obs(i_prior, i_n):
         return i_prior
     elif isinstance(i_prior, str):
         loc_val, loc_dval = _extract_val_and_dval(i_prior)
-        return cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{_rng.integers(2147483647):010d}")
+        return cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}")
     else:
         raise TypeError("Prior entries need to be 'Obs' or 'str'.")
diff --git a/pyerrors/misc.py b/pyerrors/misc.py
index 5ab1b84c..99481faf 100644
--- a/pyerrors/misc.py
+++ b/pyerrors/misc.py
@@ -10,8 +10,6 @@ import scipy
 from .obs import Obs
 from .version import __version__
 
-_rng = np.random.default_rng()
-
 
 def print_config():
     """Print information about version of python, pyerrors and dependencies."""
@@ -122,7 +120,7 @@ def pseudo_Obs(value, dvalue, name, samples=1000):
         return Obs([np.zeros(samples) + value], [name])
     else:
         for _ in range(100):
-            deltas = [_rng.normal(0.0, dvalue * np.sqrt(samples), samples)]
+            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
             deltas -= np.mean(deltas)
             deltas *= dvalue / np.sqrt(np.var(deltas) / samples) / np.sqrt(1 + 3 / samples)
             deltas += value
@@ -165,7 +163,7 @@ def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
         raise Exception('All integrated autocorrelations have to be >= 0.5.')
 
     a = (2 * tau - 1) / (2 * tau + 1)
-    rand = _rng.multivariate_normal(np.zeros_like(means), cov * samples, samples)
+    rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
 
     # Normalize samples such that sample variance matches input
     norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])
diff --git a/tests/misc_test.py b/tests/misc_test.py
index 028ea6da..99bb5056 100644
--- a/tests/misc_test.py
+++ b/tests/misc_test.py
@@ -22,18 +22,3 @@ def test_obs_errorbar():
 
 def test_print_config():
     pe.print_config()
-
-
-def test_pseudo_Obs_seed_independence():
-    # pseudo_Obs now uses a module-local np.random.default_rng() generator,
-    # so np.random.seed() no longer controls its output. The per-sample
-    # deltas therefore differ between successive calls even with a re-seed,
-    # though the normalized value / dvalue still match the requested inputs.
-    np.random.seed(0)
-    a = pe.pseudo_Obs(1.0, 0.1, "e")
-    np.random.seed(0)
-    b = pe.pseudo_Obs(1.0, 0.1, "e")
-
-    assert not np.allclose(a.deltas["e"], b.deltas["e"])
-    assert np.isclose(a.value, b.value)
-    assert np.isclose(a.dvalue, b.dvalue)