diff --git a/datascience/tables.py b/datascience/tables.py
index 51298c88..c7c0a567 100644
--- a/datascience/tables.py
+++ b/datascience/tables.py
@@ -2346,16 +2346,16 @@ def sample_from_distribution(self, distribution, k, proportions=False):
         label = self._unused_label(self._as_label(distribution) + ' sample')
         return self.with_column(label, sample)
 
-    def split(self, k):
+    def split(self, first_n):
         """Return a tuple of two tables where the first table contains
-        ``k`` rows randomly sampled and the second contains the remaining rows.
+        ``first_n`` rows randomly sampled and the second contains the remaining rows.
 
         Args:
-            ``k`` (int): The number of rows randomly sampled into the first
-                table. ``k`` must be between 1 and ``num_rows - 1``.
+            ``first_n`` (int): The number of rows randomly sampled into the first
+                table. ``first_n`` must be between 1 and ``num_rows - 1``.
 
         Raises:
-            ``ValueError``: ``k`` is not between 1 and ``num_rows - 1``.
+            ``ValueError``: ``first_n`` is not between 1 and ``num_rows - 1``.
 
         Returns:
             A tuple containing two instances of ``Table``.
@@ -2379,14 +2379,14 @@ def split(self, k):
         job  | wage
         d    | 8
         """
-        if not 1 <= k <= self.num_rows - 1:
-            raise ValueError("Invalid value of k. k must be between 1 and the"
+        if not 1 <= first_n <= self.num_rows - 1:
+            raise ValueError("Invalid value of first_n. first_n must be between 1 and the"
                              "number of rows - 1")
 
         rows = np.random.permutation(self.num_rows)
 
-        first = self.take(rows[:k])
-        rest = self.take(rows[k:])
+        first = self.take(rows[:first_n])
+        rest = self.take(rows[first_n:])
         for column_label in self._formats:
             first._formats[column_label] = self._formats[column_label]
             rest._formats[column_label] = self._formats[column_label]