diff --git a/src/arviz_plots/plots/ppcplot.py b/src/arviz_plots/plots/ppcplot.py
index b3cca40..9b32050 100644
--- a/src/arviz_plots/plots/ppcplot.py
+++ b/src/arviz_plots/plots/ppcplot.py
@@ -103,13 +103,9 @@ def plot_ppc(
     aes_map : mapping of {str : sequence of str}, optional
         Mapping of artists to aesthetics that should use their mapping in `plot_collection`
         when plotted. Valid keys are the same as for `plot_kwargs`.
-        (Note: like in `plot_kwargs` below, aes_map values are passed with key as `kind`
-        when passed to `plot_dist`)
 
     plot_kwargs : mapping of {str : mapping or False}, optional
         Valid keys are:
-        (Note: This function internally calls `plot_dist` so the first three artists here get
-        mapped to one of `plot_dist`'s visual element types.)
 
         * "predictive" -> Passed to either of "kde", "cumulative", "scatter" based on `kind`
         * "observed" -> passed to either of "kde", "cumulative", "scatter" based on `kind`
@@ -117,7 +113,6 @@ def plot_ppc(
 
         Values of the above plot_kwargs keys are passed to one of "kde", "cumulative", "scatter",
         matching the `kind` argument.
-        These are passed to :func:`~arviz_plots.plots.distplot.plot_dist`.
 
           * "kde" -> passed to :func:`~arviz_plots.visuals.line_xy`
           * "cumulative" -> passed to :func:`~arviz_plots.visuals.ecdf_line`
@@ -129,7 +124,9 @@ def plot_ppc(
     stats_kwargs : mapping, optional
         Valid keys are:
 
-        * density -> passed to kde, cumulative, ...
+        * predictive -> passed to kde, cumulative, ...
+        * aggregate -> passed to kde, cumulative, ...
+        * observed -> passed to kde, cumulative, ...
 
     pc_kwargs : mapping
         Passed to :class:`arviz_plots.PlotCollection.wrap`
@@ -150,6 +147,9 @@ def plot_ppc(
         kind = rcParams["plot.density_kind"]
     if plot_kwargs is None:
         plot_kwargs = {}
+    # Note: This function internally calls `plot_dist` so the 3 relevant artists "predictive",
+    # "aggregate", "observed" get mapped to one of `plot_dist`'s density artists- "kde",
+    # "ecdf", "scatter" based on the value of the top level arg `kind`
     if pc_kwargs is None:
         pc_kwargs = {}
     else:
@@ -371,7 +371,9 @@ def plot_ppc(
                 kind: value for key, value in aes_map.items() if key == "predictive"
             },  # aes_map[kind] is set to "predictive" aes_map
             plot_kwargs=plot_kwargs_dist,  # plot_kwargs[kind] is set to "predictive" plot_kwargs
-            stats_kwargs=stats_kwargs,  # common "density" key used for all artists generated
+            stats_kwargs={
+                "density": value for key, value in stats_kwargs.items() if key == "predictive"
+            },  # stats_kwargs["density"] is set to "predictive" stats_kwargs
             # via plot_dist
         )
 
@@ -414,7 +416,9 @@ def plot_ppc(
                 kind: value for key, value in aes_map.items() if key == "aggregate"
             },  # aes_map[kind] is set to "aggregate" aes_map
             plot_kwargs=plot_kwargs_dist,  # plot_kwargs[kind] is set to "aggregate" plot_kwargs
-            stats_kwargs=stats_kwargs,
+            stats_kwargs={
+                "density": value for key, value in stats_kwargs.items() if key == "aggregate"
+            },  # stats_kwargs["density"] is set to "aggregate" stats_kwargs
         )
 
     # ---------STEP 3 (observed data)-----------
@@ -453,7 +457,9 @@ def plot_ppc(
                 kind: value for key, value in aes_map.items() if key == "observed"
             },  # aes_map[kind] is set to "observed" aes_map
             plot_kwargs=plot_kwargs_dist,  # plot_kwargs[kind] is set to "observed" plot_kwargs
-            stats_kwargs=stats_kwargs,
+            stats_kwargs={
+                "density": value for key, value in stats_kwargs.items() if key == "observed"
+            },  # stats_kwargs["density"] is set to "observed" stats_kwargs
         )
 
     # adding plot title/s