diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index a79d201eb9a6b77e38f0cec0a269a0ca7f96478b..e1887e62eb682b9ba1cab335b690ae4de5cd7966 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -12,7 +12,7 @@ from typing import Union, Tuple, Dict, List
 from matplotlib import pyplot as plt
 import itertools
 
-from mlair.helpers import to_list, remove_items
+from mlair.helpers import to_list
 
 Data = Union[xr.DataArray, pd.DataFrame]
 
@@ -23,9 +23,11 @@ def apply_inverse_transformation(data: Data, method: str = "standardise", mean:
     Apply inverse transformation for given statistics.
 
     :param data: transform this data back
-    :param method: transformation method
-    :param mean: mean of transformation
+    :param method: transformation method (optional)
+    :param mean: mean of transformation (optional)
     :param std: standard deviation of transformation (optional)
+    :param max: maximum value for min/max transformation (optional)
+    :param min: minimum value for min/max transformation (optional)
 
     :return: inverse transformed data
     """
@@ -45,7 +47,7 @@ def standardise(data: Data, dim: Union[str, int]) -> Tuple[Data, Dict[(str, Data
 
     :param data: data to standardise
     :param dim: name (xarray) or axis (pandas) of dimension which should be standardised
-    :return: standardised data, mean, and standard deviation
+    :return: standardised data, and dictionary with keys method, mean, and standard deviation
     """
     return (data - data.mean(dim)) / data.std(dim), {"mean": data.mean(dim), "std": data.std(dim),
                                                      "method": "standardise"}
@@ -84,7 +86,7 @@ def centre(data: Data, dim: Union[str, int]) -> Tuple[Data, Dict[(str, Data)]]:
     :param data: data to centre
     :param dim: name (xarray) or axis (pandas) of dimension which should be centred
 
-    :return: centred data, mean, and None placeholder
+    :return: centred data, and dictionary with keys method, and mean
     """
     return data - data.mean(dim), {"mean": data.mean(dim), "method": "centre"}
 
@@ -114,16 +116,39 @@ def centre_apply(data: Data, mean: Data) -> Data:
 
 
 def min_max(data: Data, dim: Union[str, int]) -> Tuple[Data, Dict[(str, Data)]]:
+    """
+    Apply min/max scaling using (x - x_min) / (x_max - x_min). Returned data is in interval [0, 1].
+
+    :param data: data to transform
+    :param dim: name (xarray) or axis (pandas) of dimension which should be centred
+    :return: transformed data, and dictionary with keys method, min, and max
+    """
     d_max = data.max(dim)
     d_min = data.min(dim)
     return (data - d_min) / (d_max - d_min), {"min": d_min, "max": d_max, "method": "min_max"}
 
 
 def min_max_inverse(data: Data, min: Data, max: Data) -> Data:
+    """
+    Apply inverse transformation of `min_max` scaling.
+
+    :param data: data to apply inverse scaling
+    :param min: minimum value to use for min/max scaling
+    :param max: maximum value to use for min/max scaling
+    :return: inverted min/max scaled data
+    """
     return data * (max - min) + min
 
 
 def min_max_apply(data: Data, min: Data, max: Data) -> Data:
+    """
+    Apply `min_max` scaling with given minimum and maximum.
+
+    :param data: data to apply scaling
+    :param min: minimum value to use for min/max scaling
+    :param max: maximum value to use for min/max scaling
+    :return: min/max scaled data
+    """
     return (data - min) / (max - min)
 
 
diff --git a/mlair/run_modules/pre_processing.py b/mlair/run_modules/pre_processing.py
index bf54b0619f94d21524edc95a52c2ad49dab788c5..f696b0065b1db2692110488bd41513cd74aca233 100644
--- a/mlair/run_modules/pre_processing.py
+++ b/mlair/run_modules/pre_processing.py
@@ -186,7 +186,6 @@ class PreProcessing(RunEnvironment):
         column_format = ''.join(column_format.tolist())
         return column_format
 
-
     def split_train_val_test(self) -> None:
         """
         Split data into subsets.