diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index ae531b2526dee93672ab989d621d92eba541f19f..8d3be27ad6f1e7908041c7b5135ba965030ad8bf 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -15,6 +15,7 @@ import pandas as pd
 import seaborn as sns
 import xarray as xr
 from matplotlib.backends.backend_pdf import PdfPages
+from matplotlib.offsetbox import AnchoredText
 
 from mlair import helpers
 from mlair.data_handler.iterator import DataCollection
@@ -1001,30 +1002,33 @@ class PlotSeparationOfScales(AbstractPlotClass):
 class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass):
 
     def __init__(self, data: xr.DataArray, plot_folder: str = ".", model_type_dim: str = "type",
-                 error_measure: str = "mse", dim_name_boots: str = 'boots'):
-        plot_folder = os.path.join(plot_folder)
+                 error_measure: str = "mse", error_unit: str = None, dim_name_boots: str = 'boots'):
         super().__init__(plot_folder, "sample_uncertainty_from_bootstrap")
         self.model_type_dim = model_type_dim
         self.error_measure = error_measure
         self.dim_name_boots = dim_name_boots
+        self.error_unit = error_unit
 
         self._plot(data)
 
     def _plot(self, data):
         data_table = data.to_pandas()
+        n_boots = data_table.shape[0]
         size = max([len(np.unique(data_table.columns)), 6])
         fig, ax = plt.subplots(figsize=(size, size * 0.8))
-        # fig, ax = plt.subplots()
         sns.boxplot(data=data_table, ax=ax, whis=1., color="white",
                     showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"},
-                    flierprops={"marker": ".", "markerfacecolor": 'black', "markeredgecolor": 'none', "markersize": 1},
+                    flierprops={"marker": ".", "markerfacecolor": 'black', "markeredgecolor": 'none'},
                     width=.3)
-        ax.set_ylabel(f"{self.error_measure} " + r" in ppb$^2$")
+        ax.set_ylabel(f"{self.error_measure}  (in {self.error_unit})")
         ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
+        text_box = AnchoredText(f"n={n_boots}", frameon=True, loc=4, pad=0.5)
+        plt.setp(text_box.patch, facecolor='white', alpha=0.5)
+        ax.add_artist(text_box)
         plt.tight_layout()
         self._save()
 # a = xr.DataArray(np.array(range(20)).reshape(2,-1).T, dims={'time':range(10), 'model': ['m1', 'm2']}, coords={'time':range(10), 'model': ['m1', 'm2']})
-# create_n_bootstrap_realizations(a, dim_name_time='time', dim_name_model='model', n_boots=100)
+# data = create_n_bootstrap_realizations(a, dim_name_time='time', dim_name_model='model', n_boots=100)
 
 if __name__ == "__main__":
     stations = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index e9110b43273894365b7ef336f16096944a4a5c10..4e045df7897a7782d9e5d8a12db27df844572a77 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -539,7 +539,8 @@ class PostProcessing(RunEnvironment):
         try:
             if "PlotSampleUncertaintyFromBootstrap" in plot_list:
                 PlotSampleUncertaintyFromBootstrap(data=None, plot_folder=self.plot_path,
-                                                   model_type_dim=self.model_type_dim)
+                                                   model_type_dim=self.model_type_dim,
+                                                   error_measure="mean squared error", error_unit=r"ppb^2")
         except Exception as e:
             logging.error(f"Could not create plot PlotSampleUncertaintyFromBootstrap due to the following error: {e}"
                           f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")