diff --git a/docs/examples/getting-started.ipynb b/docs/examples/getting-started.ipynb
index 549e938..e615dc8 100644
--- a/docs/examples/getting-started.ipynb
+++ b/docs/examples/getting-started.ipynb
@@ -107,8 +107,8 @@
    "source": [
     "print(f\"Clusters: {result.n_clusters}\")\n",
     "print(f\"Timesteps per period: {result.n_timesteps_per_period}\")\n",
-    "print(\"Cluster weights (days each represents):\")\n",
-    "result.cluster_counts.to_dataframe(\"weight\")"
+    "print(\"Cluster counts (days each represents):\")\n",
+    "result.cluster_counts.to_dataframe(\"count\")"
    ]
   },
   {
diff --git a/docs/examples/multi-dim.ipynb b/docs/examples/multi-dim.ipynb
index 52113c3..231b6f1 100644
--- a/docs/examples/multi-dim.ipynb
+++ b/docs/examples/multi-dim.ipynb
@@ -87,7 +87,7 @@
     "coherent multi-dimensional arrays.\n",
     "\n",
     "Here, `scenario` is auto-sliced. Each scenario gets its own clustering.\n",
-    "Cluster weights, accuracy metrics, and cluster representatives all have the `scenario`\n",
+    "Cluster counts, accuracy metrics, and cluster representatives all have the `scenario`\n",
     "dimension — no manual looping or concatenation needed.\n",
     "\n",
     "Without tsam_xarray, you'd have to:\n",
@@ -115,7 +115,7 @@
     "    n_clusters=4,\n",
     ")\n",
     "print(\"Result dims:\", result_sliced.cluster_representatives.dims)\n",
-    "result_sliced.cluster_counts.to_dataframe(\"weight\")"
+    "result_sliced.cluster_counts.to_dataframe(\"count\")"
    ]
   },
   {
diff --git a/test/test_parametrized.py b/test/test_parametrized.py
index dc0c2f9..631dbde 100644
--- a/test/test_parametrized.py
+++ b/test/test_parametrized.py
@@ -299,7 +299,7 @@ def test_cluster_occurrences_dims(self, agg_case: AggregateCase):
         da = result.clustering.cluster_occurrences
         assert set(da.dims) == {"cluster"} | agg_case.expected_slice_dims
 
-    def test_cluster_occurrences_matches_weights(self, agg_case: AggregateCase):
+    def test_cluster_occurrences_matches_counts(self, agg_case: AggregateCase):
         """Occurrences match AggregationResult.cluster_counts."""
         result = _aggregate(agg_case)
         np.testing.assert_array_equal(