diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst
index 27d0518..0798ae2 100644
--- a/Doc/library/statistics.rst
+++ b/Doc/library/statistics.rst
@@ -19,17 +19,21 @@
 --------------
 
 This module provides functions for calculating mathematical statistics of
-numeric (:class:`Real`-valued) data.
+numeric (:class:`~numbers.Real`-valued) data.
 
-.. note::
+The module is not intended to be a competitor to third-party libraries such
+as `NumPy <https://numpy.org>`_, `SciPy <https://www.scipy.org/>`_, or
+proprietary full-featured statistics packages aimed at professional
+statisticians such as Minitab, SAS and Matlab. It is aimed at the level of
+graphing and scientific calculators.
 
-   Unless explicitly noted otherwise, these functions support :class:`int`,
-   :class:`float`, :class:`decimal.Decimal` and :class:`fractions.Fraction`.
-   Behaviour with other types (whether in the numeric tower or not) is
-   currently unsupported.  Collections with a mix of types are also undefined
-   and implementation-dependent.  If your input data consists of mixed types,
-   you may be able to use :func:`map` to ensure a consistent result, for
-   example: ``map(float, input_data)``.
+Unless explicitly noted, these functions support :class:`int`,
+:class:`float`, :class:`~decimal.Decimal` and :class:`~fractions.Fraction`.
+Behaviour with other types (whether in the numeric tower or not) is
+currently unsupported.  Collections with a mix of types are also undefined
+and implementation-dependent.  If your input data consists of mixed types,
+you may be able to use :func:`map` to ensure a consistent result, for
+example: ``map(float, input_data)``.
 
 Averages and measures of central location
 -----------------------------------------
@@ -107,7 +111,7 @@
       :func:`median` and :func:`mode`.
 
       The sample mean gives an unbiased estimate of the true population mean,
-      which means that, taken on average over all the possible samples,
+      so that when taken on average over all the possible samples,
       ``mean(sample)`` converges on the true mean of the entire population.  If
       *data* represents the entire population rather than a sample, then
       ``mean(data)`` is equivalent to calculating the true population mean μ.
@@ -163,8 +167,16 @@
    will be equivalent to ``3/(1/a + 1/b + 1/c)``.
 
    The harmonic mean is a type of average, a measure of the central
-   location of the data.  It is often appropriate when averaging quantities
-   which are rates or ratios, for example speeds. For example:
+   location of the data.  It is often appropriate when averaging
+   rates or ratios, for example speeds.
+
+   Suppose a car travels 10 km at 40 km/hr, then another 10 km at 60 km/hr.
+   What is the average speed?
+
+   .. doctest::
+
+      >>> harmonic_mean([40, 60])
+      48.0
 
    Suppose an investor purchases an equal value of shares in each of
    three companies, with P/E (price/earning) ratios of 2.5, 3 and 10.
@@ -175,9 +187,6 @@
       >>> harmonic_mean([2.5, 3, 10])  # For an equal investment portfolio.
       3.6
 
-   Using the arithmetic mean would give an average of about 5.167, which
-   is well over the aggregate P/E ratio.
-
    :exc:`StatisticsError` is raised if *data* is empty, or any element
    is less than zero.
 
@@ -190,9 +199,9 @@
    middle two" method.  If *data* is empty, :exc:`StatisticsError` is raised.
    *data* can be a sequence or iterator.
 
-   The median is a robust measure of central location, and is less affected by
-   the presence of outliers in your data.  When the number of data points is
-   odd, the middle data point is returned:
+   The median is a robust measure of central location and is less affected by
+   the presence of outliers.  When the number of data points is odd, the
+   middle data point is returned:
 
    .. doctest::
 
@@ -210,13 +219,10 @@
    This is suited for when your data is discrete, and you don't mind that the
    median may not be an actual data point.
 
-   If your data is ordinal (supports order operations) but not numeric (doesn't
-   support addition), you should use :func:`median_low` or :func:`median_high`
+   If the data is ordinal (supports order operations) but not numeric (doesn't
+   support addition), consider using :func:`median_low` or :func:`median_high`
    instead.
 
-   .. seealso:: :func:`median_low`, :func:`median_high`, :func:`median_grouped`
-
-
 .. function:: median_low(data)
 
    Return the low median of numeric data.  If *data* is empty,
@@ -319,7 +325,7 @@
    desired instead, use ``min(multimode(data))`` or ``max(multimode(data))``.
    If the input *data* is empty, :exc:`StatisticsError` is raised.
 
-   ``mode`` assumes discrete data, and returns a single value. This is the
+   ``mode`` assumes discrete data and returns a single value. This is the
    standard treatment of the mode as commonly taught in schools:
 
    .. doctest::
@@ -522,7 +528,7 @@
    cut-point will evaluate to ``104``.
 
    The *method* for computing quantiles can be varied depending on
-   whether the data in *data* includes or excludes the lowest and
+   whether the *data* includes or excludes the lowest and
    highest possible values from the population.
 
    The default *method* is "exclusive" and is used for data sampled from