diff --git a/doc/source/basics.rst b/doc/source/basics.rst
index 9ecee2ea86bd1..68ab07bc3df91 100644
--- a/doc/source/basics.rst
+++ b/doc/source/basics.rst
@@ -483,11 +483,11 @@ optional ``level`` parameter which applies only if the object has a
     ``mode``, Mode
     ``abs``, Absolute Value
     ``prod``, Product of values
-    ``std``, Unbiased standard deviation
+    ``std``, Bessel-corrected sample standard deviation
     ``var``, Unbiased variance
-    ``sem``, Unbiased standard error of the mean
-    ``skew``, Unbiased skewness (3rd moment)
-    ``kurt``, Unbiased kurtosis (4th moment)
+    ``sem``, Standard error of the mean
+    ``skew``, Sample skewness (3rd moment)
+    ``kurt``, Sample kurtosis (4th moment)
     ``quantile``, Sample quantile (value at %)
     ``cumsum``, Cumulative sum
     ``cumprod``, Cumulative product
diff --git a/doc/source/computation.rst b/doc/source/computation.rst
index 097898f027429..ed3251efc3656 100644
--- a/doc/source/computation.rst
+++ b/doc/source/computation.rst
@@ -309,10 +309,10 @@ We provide a number of the common statistical functions:
     :meth:`~Rolling.median`, Arithmetic median of values
     :meth:`~Rolling.min`, Minimum
     :meth:`~Rolling.max`, Maximum
-    :meth:`~Rolling.std`, Unbiased standard deviation
+    :meth:`~Rolling.std`, Bessel-corrected sample standard deviation
     :meth:`~Rolling.var`, Unbiased variance
-    :meth:`~Rolling.skew`, Unbiased skewness (3rd moment)
-    :meth:`~Rolling.kurt`, Unbiased kurtosis (4th moment)
+    :meth:`~Rolling.skew`, Sample skewness (3rd moment)
+    :meth:`~Rolling.kurt`, Sample kurtosis (4th moment)
     :meth:`~Rolling.quantile`, Sample quantile (value at %)
     :meth:`~Rolling.apply`, Generic apply
     :meth:`~Rolling.cov`, Unbiased covariance (binary)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index ce156232ed698..1bea2fb4fcf95 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -4783,7 +4783,7 @@ def mad(self, axis=None, skipna=None, level=None):
             nanops.nanvar)
         cls.std = _make_stat_function_ddof(
             'std', name, name2, axis_descr,
-            "Return unbiased standard deviation over requested axis."
+            "Return sample standard deviation over requested axis."
             "\n\nNormalized by N-1 by default. This can be changed using the "
             "ddof argument",
             nanops.nanstd)