title: stats.chisquare wrongly returns p_value = 0
body: <p>I have such expected and frequencies in each category as shown below:</p>
  
  <p><a href="https://i.stack.imgur.com/o0hZO.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/o0hZO.png" alt="enter image description here"></a> </p>
  
  <p><a href="https://i.stack.imgur.com/ulXHo.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ulXHo.png" alt="enter image description here"></a></p>
  
  <p>They seem to be pretty similar, but chi square test doesn't think so:</p>
  
  <pre><code>stats.chisquare(city_ans.answered, city_ans.sent)
  # Power_divergenceResult(statistic=893005.32003277098, pvalue=0.0)
  </code></pre>
  
  <p>Filtering values less than 10 doesn't change much:</p>
  
  <pre><code>stats.chisquare(city_ans[city_ans.answered &gt; 10].answered, city_ans[city_ans.answered &gt; 10].sent)
  
  # Power_divergenceResult(statistic=892917.3023088905, pvalue=0.0)
  </code></pre>
  
  <p>What am I doing wrong? 
  How can I apply F - test here?</p>
  
  <p>PS. Using this test with the same values as obs and exp - return p_val = 1</p>
  
  <pre><code>stats.chisquare(city_ans.sent, city_ans.sent)
  # Power_divergenceResult(statistic=0.0, pvalue=1.0)
  </code></pre>
  
  <p>However such code also should return p_val = 1, but it doesn't:</p>
  
  <pre><code>stats.chisquare(0.9*city_ans.sent, city_ans.sent)
  # Power_divergenceResult(statistic=10591.249999999995, pvalue=0.0)
  </code></pre>
  
  <p><strong>UPD:</strong></p>
  
  <pre><code>f_obs = weeks_ans.answered / np.sum(weeks_ans.answered)
  f_exp = weeks_ans.sent / np.sum(weeks_ans.sent)
  stats.chisquare(f_obs, f_exp)
  
  # Power_divergenceResult(statistic=0.064491479990141154, pvalue=1.0)
  </code></pre>
  
  <p>Its seems to pretty nice result but I think something isnt right here.</p>
  
  <p>For example look to that distribution below, same approach:</p>
  
  <pre><code># Power_divergenceResult(statistic=0.064491479990141154, pvalue=1.0)
  </code></pre>
  
  <p>Is it fair to use such test that the distribution are the same</p>
  
  <p><a href="https://i.stack.imgur.com/sryvI.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/sryvI.png" alt="enter image description here"></a></p>