Add an example of conf interval in the guide

doc/guide.rst

@@ -228,6 +228,17 @@ It is based on the cumulative probabilities of the binomial distribution. This
 method is quite conservative, meaning that the true coverage rate of a 95% 
 Clopper–Pearson interval may be well above 95%.
 
+For example, we want to evaluate the reliability of a system to
+identify registered persons. Let's say that among 10,000 accepted
+transactions, 9856 are true matches. The 95% confidence interval for true match
+rate is then:
+.. doctest:: python
+
+    >>> bob.measure.utils.confidence_for_indicator_variable(9856, 10000)
+    (0.98306835053282549, 0.98784270928084694)
+
+meaning there is a 95% probability that the true match rate is inside :math:`[0.983,
+0.988]`.
 
 Plotting
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