diff --git a/README.html b/README.html
index afc9e14..19ccbc8 100644
--- a/README.html
+++ b/README.html
@@ -958,9 +958,6 @@ the rows which only contain integers and which sum to n. (★★★)</p>
 <li><p class="first">Compute bootstrapped 95% confidence intervals for the mean of a 1D array X
 (i.e., resample the elements of an array with replacement N times, compute
 the mean of each sample, and then compute percentiles over the means). (★★★)</p>
-</li>
-</ol>
-<blockquote>
 <pre class="code python literal-block">
 <span class="comment single"># Author: Jessica B. Hamrick</span>
 
@@ -971,7 +968,8 @@ the mean of each sample, and then compute percentiles over the means). (★★
 <span class="name">confint</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">percentile</span><span class="punctuation">(</span><span class="name">means</span><span class="punctuation">,</span> <span class="punctuation">[</span><span class="literal number float">2.5</span><span class="punctuation">,</span> <span class="literal number float">97.5</span><span class="punctuation">])</span>
 <span class="keyword">print</span><span class="punctuation">(</span><span class="name">confint</span><span class="punctuation">)</span>
 </pre>
-</blockquote>
+</li>
+</ol>
 </div>
 </body>
 </html>
diff --git a/README.rst b/README.rst
index 27969f4..d5ef71a 100644
--- a/README.rst
+++ b/README.rst
@@ -1118,13 +1118,13 @@ Thanks to Michiaki Ariga, there is now a
    (i.e., resample the elements of an array with replacement N times, compute
    the mean of each sample, and then compute percentiles over the means). (★★★)
 
-  .. code-block:: python
+   .. code-block:: python
 
-     # Author: Jessica B. Hamrick
+      # Author: Jessica B. Hamrick
 
-     X = np.random.randn(100) # random 1D array
-     N = 1000 # number of bootstrap samples
-     idx = np.random.randint(0, X.size, (N, X.size))
-     means = X[idx].mean(axis=1)
-     confint = np.percentile(means, [2.5, 97.5])
-     print(confint)
+      X = np.random.randn(100) # random 1D array
+      N = 1000 # number of bootstrap samples
+      idx = np.random.randint(0, X.size, (N, X.size))
+      means = X[idx].mean(axis=1)
+      confint = np.percentile(means, [2.5, 97.5])
+      print(confint)