<p dir="auto">The Geometric and Harmonic means have two sets of test cases in <code class="notranslate">test_stats</code>. Ideally, the tests for related functions should occur in one place, not spread across multiple suites. I think these tests should be reviewed and combined into a single test suit where possible.</p>
<p dir="auto">For reference, the tests covering the geometric mean are  <code class="notranslate">TestGMean</code> and <code class="notranslate">GeoMeanTestCase</code> / <code class="notranslate">TestGeoMean(GeoMeanTestCase)</code>. And for the harmonic mean <code class="notranslate">TestHMean</code> and <code class="notranslate">HarMeanTestCase</code> / <code class="notranslate">TestHarMean(HarMeanTestCase)</code></p>
<p dir="auto">Currently, <code class="notranslate">scipy.stats</code> has a function <code class="notranslate">variation</code>, which computes the coefficient of variation (<code class="notranslate">std/mean</code>).</p>
<p dir="auto">However, there is a closely-related function called the Fano factor (<a href="http://en.wikipedia.org/wiki/Fano_factor" rel="nofollow">http://en.wikipedia.org/wiki/Fano_factor</a>).  While the coefficient of variation is <code class="notranslate">std/mean</code>, the Fano factor is <code class="notranslate">var/mean</code>.  Different random distributions have different Fano factors.  It is particular important as a measure of how well similar a random process is to a Poisson process (which is important for everything from particle detectors to neurons), since a purely Poisson process has a Fano factor of 1.</p>
<p dir="auto">So I think it would be good to have a <code class="notranslate">fano</code> function in <code class="notranslate">scipy.stats</code>, to give the counterpart to the <code class="notranslate">variation</code> function but more importantly because Poisson processes are critically important to many fields.</p>