diff --git a/docs/source/tutorials/day_of_the_week.qmd b/docs/source/tutorials/day_of_the_week.qmd
index 0b96008f..f6558698 100644
--- a/docs/source/tutorials/day_of_the_week.qmd
+++ b/docs/source/tutorials/day_of_the_week.qmd
@@ -283,8 +283,6 @@ As a result, we can see the posterior distribution of our novel day-of-the-week
 out = hosp_model_dow.plot_posterior(
     var="dayofweek_effect_raw", ylab="Day of the Week Effect", samples=500
 )
-
-sp = hosp_model_dow.spread_draws(["dayofweek_effect"])
 ```
 
 The new model with the day-of-the-week effect can be compared to the previous model without the effect. Finally, let's reproduce the figure without the day-of-the-week effect, and then plot the new model with the effect:
diff --git a/test/test_model_basic_renewal.py b/test/test_model_basic_renewal.py
index dfc33c3b..5aaf2cce 100644
--- a/test/test_model_basic_renewal.py
+++ b/test/test_model_basic_renewal.py
@@ -7,7 +7,6 @@
 import numpy as np
 import numpyro
 import numpyro.distributions as dist
-import polars as pl
 import pytest
 
 from pyrenew.deterministic import DeterministicPMF, NullObservation
@@ -157,17 +156,6 @@ def test_model_basicrenewal_no_obs_model():
         data_observed_infections=model0_samp.latent_infections,
     )
 
-    inf = model0.spread_draws(["all_latent_infections"])
-    inf_mean = (
-        inf.group_by("draw")
-        .agg(pl.col("all_latent_infections").mean())
-        .sort(pl.col("draw"))
-    )
-
-    # For now the assertion is only about the expected number of rows
-    # It should be about the MCMC inference.
-    assert inf_mean.to_numpy().shape[0] == 500
-
 
 def test_model_basicrenewal_with_obs_model():
     """
@@ -215,17 +203,6 @@ def test_model_basicrenewal_with_obs_model():
         data_observed_infections=model1_samp.observed_infections,
     )
 
-    inf = model1.spread_draws(["all_latent_infections"])
-    inf_mean = (
-        inf.group_by("draw")
-        .agg(pl.col("all_latent_infections").mean())
-        .sort(pl.col("draw"))
-    )
-
-    # For now the assertion is only about the expected number of rows
-    # It should be about the MCMC inference.
-    assert inf_mean.to_numpy().shape[0] == 500
-
 
 def test_model_basicrenewal_padding() -> None:  # numpydoc ignore=GL08
     gen_int = DeterministicPMF(
@@ -264,15 +241,3 @@ def test_model_basicrenewal_padding() -> None:  # numpydoc ignore=GL08
         data_observed_infections=model1_samp.observed_infections,
         padding=pad_size,
     )
-
-    inf = model1.spread_draws(["all_latent_infections"])
-
-    inf_mean = (
-        inf.group_by("draw")
-        .agg(pl.col("all_latent_infections").mean())
-        .sort(pl.col("draw"))
-    )
-
-    # For now the assertion is only about the expected number of rows
-    # It should be about the MCMC inference.
-    assert inf_mean.to_numpy().shape[0] == 500
diff --git a/test/test_model_hosp_admissions.py b/test/test_model_hosp_admissions.py
index 4aed146c..60293b07 100644
--- a/test/test_model_hosp_admissions.py
+++ b/test/test_model_hosp_admissions.py
@@ -7,7 +7,6 @@
 import numpy as np
 import numpyro
 import numpyro.distributions as dist
-import polars as pl
 import pytest
 
 from pyrenew.deterministic import (
@@ -255,17 +254,6 @@ def test_model_hosp_no_obs_model():
         data_observed_hosp_admissions=model0_samp.latent_hosp_admissions,
     )
 
-    inf = model0.spread_draws(["latent_hospital_admissions"])
-    inf_mean = (
-        inf.group_by("draw")
-        .agg(pl.col("latent_hospital_admissions").mean())
-        .sort(pl.col("draw"))
-    )
-
-    # For now the assertion is only about the expected number of rows
-    # It should be about the MCMC inference.
-    assert inf_mean.to_numpy().shape[0] == 500
-
 
 def test_model_hosp_with_obs_model():
     """
@@ -342,17 +330,6 @@ def test_model_hosp_with_obs_model():
         data_observed_hosp_admissions=model1_samp.observed_hosp_admissions,
     )
 
-    inf = model1.spread_draws(["latent_hospital_admissions"])
-    inf_mean = (
-        inf.group_by("draw")
-        .agg(pl.col("latent_hospital_admissions").mean())
-        .sort(pl.col("draw"))
-    )
-
-    # For now the assertion is only about the expected number of rows
-    # It should be about the MCMC inference.
-    assert inf_mean.to_numpy().shape[0] == 500
-
 
 def test_model_hosp_with_obs_model_weekday_phosp_2():
     """
@@ -436,17 +413,6 @@ def test_model_hosp_with_obs_model_weekday_phosp_2():
         data_observed_hosp_admissions=model1_samp.observed_hosp_admissions,
     )
 
-    inf = model1.spread_draws(["latent_hospital_admissions"])
-    inf_mean = (
-        inf.group_by("draw")
-        .agg(pl.col("latent_hospital_admissions").mean())
-        .sort(pl.col("draw"))
-    )
-
-    # For now the assertion is only about the expected number of rows
-    # It should be about the MCMC inference.
-    assert inf_mean.to_numpy().shape[0] == 500
-
 
 def test_model_hosp_with_obs_model_weekday_phosp():
     """
@@ -558,14 +524,3 @@ def test_model_hosp_with_obs_model_weekday_phosp():
         data_observed_hosp_admissions=model1_samp.observed_hosp_admissions,
         padding=pad_size,
     )
-
-    inf = model1.spread_draws(["latent_hospital_admissions"])
-    inf_mean = (
-        inf.group_by("draw")
-        .agg(pl.col("latent_hospital_admissions").mean())
-        .sort(pl.col("draw"))
-    )
-
-    # For now the assertion is only about the expected number of rows
-    # It should be about the MCMC inference.
-    assert inf_mean.to_numpy().shape[0] == 500