diff --git a/src/safeds/data/tabular/containers/_table.py b/src/safeds/data/tabular/containers/_table.py
index fd62f988f..fbfeeb962 100644
--- a/src/safeds/data/tabular/containers/_table.py
+++ b/src/safeds/data/tabular/containers/_table.py
@@ -596,7 +596,9 @@ def drop_columns_with_non_numerical_values(self) -> Table:
             A table without the columns that contain non-numerical values.
 
         """
-        return Table.from_columns(self._list_columns_with_numerical_values())
+        return Table.from_columns(
+            [column for column in self.to_columns() if column.type.is_numeric()]
+        )
 
     def drop_duplicate_rows(self) -> Table:
         """
@@ -626,27 +628,26 @@ def drop_rows_with_missing_values(self) -> Table:
 
     def drop_rows_with_outliers(self) -> Table:
         """
-        Remove all rows from the table that contain at least one outlier defined as having a value that has a distance
-        of more than 3 standard deviations from the column average.
+        Remove all rows from the table that contain at least one outlier.
+
+        We define an outlier as a value that has a distance of more than 3 standard deviations from the column mean.
+        Missing values are not considered outliers. They are also ignored during the calculation of the standard
+        deviation.
 
         Returns
         -------
         new_table : Table
             A new table without rows containing outliers.
         """
-        result = self._data.copy(deep=True)
+        copy = self._data.copy(deep=True)
 
-        table_without_nonnumericals = Table.from_columns(
-            self._list_columns_with_numerical_values()
+        table_without_nonnumericals = self.drop_columns_with_non_numerical_values()
+        z_scores = np.absolute(
+            stats.zscore(table_without_nonnumericals._data, nan_policy="omit")
         )
+        filter_ = ((z_scores < 3) | np.isnan(z_scores)).all(axis=1)
 
-        result = result[
-            (np.absolute(stats.zscore(table_without_nonnumericals._data)) < 3).all(
-                axis=1
-            )
-        ]
-
-        return Table(result, self._schema)
+        return Table(copy[filter_], self._schema)
 
     def filter_rows(self, query: Callable[[Row], bool]) -> Table:
         """
@@ -1098,18 +1099,3 @@ def _ipython_display_(self) -> DisplayHandle:
             "display.max_rows", tmp.shape[0], "display.max_columns", tmp.shape[1]
         ):
             return display(tmp)
-
-    def _list_columns_with_numerical_values(self) -> list[Column]:
-        """
-        Return a list of columns only containing numerical values.
-
-        Returns
-        -------
-        cols : list[Column]
-            The list with only numerical columns.
-        """
-        cols = []
-        for column_name, data_type in self._schema._schema.items():
-            if data_type.is_numeric():
-                cols.append(self.get_column(column_name))
-        return cols
diff --git a/tests/safeds/data/tabular/containers/_table/test_drop_rows_with_outliers.py b/tests/safeds/data/tabular/containers/_table/test_drop_rows_with_outliers.py
index 25b12a458..cfe37163c 100644
--- a/tests/safeds/data/tabular/containers/_table/test_drop_rows_with_outliers.py
+++ b/tests/safeds/data/tabular/containers/_table/test_drop_rows_with_outliers.py
@@ -22,7 +22,7 @@ def test_drop_rows_with_outliers_no_outliers() -> None:
 
 
 def test_drop_rows_with_outliers_with_outliers() -> None:
-    table = Table(
+    input_ = Table(
         pd.DataFrame(
             data={
                 "col1": [
@@ -39,14 +39,24 @@ def test_drop_rows_with_outliers_with_outliers() -> None:
                     "a",
                     "a",
                 ],
-                "col2": [1.0, 2.0, 3.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+                "col2": [1.0, 2.0, 3.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, None],
                 "col3": [2, 3, 1, 1_000_000_000, 1, 1, 1, 1, 1, 1, 1, 1],
             }
         )
     )
-    result = table.drop_rows_with_outliers()
-    assert result.count_rows() == 11
-    assert result.count_columns() == 3
+    result = input_.drop_rows_with_outliers()
+
+    expected = Table(
+        pd.DataFrame(
+            data={
+                "col1": ["A", "B", "C", "a", "a", "a", "a", "a", "a", "a", "a"],
+                "col2": [1.0, 2.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, None],
+                "col3": [2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+            }
+        )
+    )
+
+    assert result == expected
 
 
 def test_drop_rows_with_outliers_no_rows() -> None:
diff --git a/tests/safeds/data/tabular/containers/_table/test_list_columns_with_numerical_values.py b/tests/safeds/data/tabular/containers/_table/test_list_columns_with_numerical_values.py
deleted file mode 100644
index bc5cba885..000000000
--- a/tests/safeds/data/tabular/containers/_table/test_list_columns_with_numerical_values.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import numpy as np
-import pandas as pd
-from safeds.data.tabular.containers import Table
-from safeds.data.tabular.typing import ColumnType, TableSchema
-
-
-def test_list_columns_with_numerical_values_valid() -> None:
-    table = Table(
-        pd.DataFrame(
-            data={
-                "col1": ["A", "B", "C", "A"],
-                "col2": ["Test1", "Test1", "Test3", "Test1"],
-                "col3": [1, 2, 3, 4],
-                "col4": [2, 3, 1, 4],
-            }
-        )
-    )
-    columns = table._list_columns_with_numerical_values()
-    assert columns[0] == table.get_column("col3")
-    assert columns[1] == table.get_column("col4")
-    assert len(columns) == 2
-
-
-def test_list_columns_with_numerical_values_invalid() -> None:
-    table = Table(
-        [], TableSchema({"col1": ColumnType.from_numpy_dtype(np.dtype(float))})
-    )
-    columns = table._list_columns_with_numerical_values()
-    assert columns[0] == table.get_column("col1")
-    assert len(columns) == 1