diff --git a/pymc/tests/test_model.py b/pymc/tests/test_model.py
index edb2888834..fe3c711684 100644
--- a/pymc/tests/test_model.py
+++ b/pymc/tests/test_model.py
@@ -1263,8 +1263,9 @@ def test_interval_missing_observations():
 
         # Make sure that the observed values are newly generated samples and that
         # the observed and deterministic matche
-        pp_trace = pm.sample_posterior_predictive(
-            trace, return_inferencedata=False, keep_size=False
+        pp_idata = pm.sample_posterior_predictive(trace)
+        pp_trace = pp_idata.posterior_predictive.stack(sample=["chain", "draw"]).transpose(
+            "sample", ...
         )
         assert np.all(np.var(pp_trace["theta1"], 0) > 0.0)
         assert np.all(np.var(pp_trace["theta2"], 0) > 0.0)
diff --git a/pymc/tests/test_sampling.py b/pymc/tests/test_sampling.py
index e508bc015b..9c06b4244f 100644
--- a/pymc/tests/test_sampling.py
+++ b/pymc/tests/test_sampling.py
@@ -505,21 +505,6 @@ def test_partial_trace_sample():
         assert "b" not in idata.posterior
 
 
-def test_chain_idx():
-    # see https://github.com/pymc-devs/pymc/issues/4469
-    with pm.Model():
-        mu = pm.Normal("mu")
-        x = pm.Normal("x", mu=mu, sigma=1, observed=np.asarray(3))
-        # note draws-tune must be >100 AND we need an observed RV for this to properly
-        # trigger convergence checks, which is one particular case in which this failed
-        # before
-        idata = pm.sample(draws=150, tune=10, chain_idx=1)
-
-        ppc = pm.sample_posterior_predictive(idata)
-        # TODO FIXME: Assert something.
-        ppc = pm.sample_posterior_predictive(idata)
-
-
 @pytest.mark.parametrize(
     "n_points, tune, expected_length, expected_n_traces",
     [
@@ -655,7 +640,8 @@ def test_normal_scalar(self):
             assert ppc["a"].shape == (nchains, ndraws)
 
             # test default case
-            ppc = pm.sample_posterior_predictive(trace, var_names=["a"])
+            idata_ppc = pm.sample_posterior_predictive(trace, var_names=["a"])
+            ppc = idata_ppc.posterior_predictive
             assert "a" in ppc
             assert ppc["a"].shape == (nchains, ndraws)
             # mu's standard deviation may have changed thanks to a's observed