diff --git a/src/arviz_stats/base/array.py b/src/arviz_stats/base/array.py
index 33a43ec..11c74ff 100644
--- a/src/arviz_stats/base/array.py
+++ b/src/arviz_stats/base/array.py
@@ -62,7 +62,7 @@ def hdi(
             "multimodal": (
                 self._hdi_multimodal_discrete if is_discrete else self._hdi_multimodal_continuous
             ),
-            "multimodal_nearest": (
+            "multimodal_sample": (
                 self._hdi_multimodal_discrete if is_discrete else self._hdi_multimodal_continuous
             ),
         }[method]
@@ -87,8 +87,8 @@ def hdi(
                 func_kwargs["bw"] = "isj" if not circular else "taylor"
             func_kwargs.update(kwargs)
 
-        if method == "multimodal_nearest":
-            func_kwargs["nearest"] = True
+        if method == "multimodal_sample":
+            func_kwargs["from_sample"] = True
 
         result = hdi_array(ary, **func_kwargs)
         if is_multimodal:
diff --git a/src/arviz_stats/base/core.py b/src/arviz_stats/base/core.py
index 24de035..422c297 100644
--- a/src/arviz_stats/base/core.py
+++ b/src/arviz_stats/base/core.py
@@ -247,7 +247,7 @@ def _hdi_nearest(self, ary, prob, circular, skipna):
         return hdi_interval
 
     def _hdi_multimodal_continuous(
-        self, ary, prob, skipna, max_modes, circular, nearest=False, **kwargs
+        self, ary, prob, skipna, max_modes, circular, from_sample=False, **kwargs
     ):
         """Compute HDI if the distribution is multimodal."""
         ary = ary.flatten()
@@ -255,7 +255,7 @@ def _hdi_multimodal_continuous(
             ary = ary[~np.isnan(ary)]
 
         bins, density, _ = self.kde(ary, circular=circular, **kwargs)
-        if nearest:
+        if from_sample:
             ary_density = np.interp(ary, bins, density)
             hdi_intervals, interval_probs = self._hdi_from_point_densities(
                 ary, ary_density, prob, circular
diff --git a/tests/base/test_stats.py b/tests/base/test_stats.py
index 00d04d6..925ba1f 100644
--- a/tests/base/test_stats.py
+++ b/tests/base/test_stats.py
@@ -92,7 +92,7 @@ def test_hdi_coords(centered_eight):
 @pytest.mark.parametrize("prob", [0.56, 0.83])
 @pytest.mark.parametrize("nearest", [True, False])
 def test_hdi_multimodal_continuous(prob, nearest):
-    method = "multimodal_nearest" if nearest else "multimodal"
+    method = "multimodal_sample" if nearest else "multimodal"
     rng = np.random.default_rng(43)
     dist1 = norm(loc=-30, scale=0.5)
     dist2 = norm(loc=30, scale=0.5)
@@ -176,7 +176,8 @@ def test_hdi_multimodal_max_modes():
     sample = ndarray_to_dataarray(x, "x", sample_dims=["sample"])
     intervals = sample.azstats.hdi(dims="sample", method="multimodal", prob=0.9)
     assert intervals.sizes["mode"] == 2
-    intervals2 = sample.azstats.hdi(dims="sample", method="multimodal", prob=0.9, max_modes=1)
+    with pytest.warns(UserWarning, match="found more modes"):
+        intervals2 = sample.azstats.hdi(dims="sample", method="multimodal", prob=0.9, max_modes=1)
     assert intervals2.sizes["mode"] == 1
     assert intervals2.equals(intervals.isel(mode=[1]))
 
@@ -194,7 +195,7 @@ def test_hdi_multimodal_circular(nearest):
         "x",
         sample_dims=["sample"],
     )
-    method = "multimodal_nearest" if nearest else "multimodal"
+    method = "multimodal_sample" if nearest else "multimodal"
     interval = normal_sample.azstats.hdi(circular=True, method=method, prob=0.83, dims="sample")
     assert interval.sizes["mode"] == 2
     assert interval.sel(mode=0)[0] <= np.pi / 2 <= interval.sel(mode=0)[1]