diff --git a/examples/statistics/histogram_demo_features.py b/examples/statistics/histogram_demo_features.py
index 32c56cb81d4..681cb0f7e15 100644
--- a/examples/statistics/histogram_demo_features.py
+++ b/examples/statistics/histogram_demo_features.py
@@ -27,7 +27,7 @@
 y = mlab.normpdf(bins, mu, sigma)
 plt.plot(bins, y, '--')
 plt.xlabel('Smarts')
-plt.ylabel('Probability')
+plt.ylabel('Probability density')
 plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
 
 # Tweak spacing to prevent clipping of ylabel
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 4f620ba3199..080f88a404c 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -5801,9 +5801,11 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
         normed : boolean, optional
             If `True`, the first element of the return tuple will
             be the counts normalized to form a probability density, i.e.,
-            ``n/(len(x)`dbin)``, i.e., the integral of the histogram will sum
-            to 1. If *stacked* is also *True*, the sum of the histograms is
-            normalized to 1.
+            the area (or integral) under the histogram will sum to 1.
+            This is achieved dividing the count by the number of observations
+            times the bin width and *not* dividing by the total number
+            of observations. If `stacked` is also `True`, the sum of the
+            histograms is normalized to 1.
 
             Default is ``False``