title: How to interpret scipy.stats.probplot results?
body: <p>I wanted to use <code>scipy.stats.probplot()</code> to perform some gaussianity test on <code>mydata</code>.</p>
  <pre><code>from scipy import stats
  _,fit=stats.probplot(mydata, dist=stats.norm,plot=ax)
  goodness_fit=&quot;%.2f&quot; %fit[2]
  </code></pre>
  <p>The documentation says:</p>
  <blockquote>
  <p><strong>Generates a probability plot</strong> of sample data against the quantiles of a
  specified theoretical distribution (the normal distribution by
  default). probplot optionally calculates a best-fit line for the data
  and plots the results using Matplotlib or a given plot function.
  probplot generates a probability plot, <strong>which should not be confused
  with a Q-Q or a P-P plot</strong>. Statsmodels has more extensive functionality
  of this type, see statsmodels.api.ProbPlot.</p>
  </blockquote>
  <p>But if google <strong>probability plot</strong>, it is a common name for P-P plot, while the documentation says not to confuse the two things.</p>
  <p>Now I am confused, what is this function doing?</p>