title: How to calculate statistical significance of conversion rate of several goals?
body: <p>I am making a python/django split testing or a/b testing library for my own use. However I don't know how to calculate if my test is statisically significant. I have no knowledge of statistics, so I cannot understand most wikipedia articles, or web pages on this topic, so I'm looking for help from my fellow programmers.</p>
  
  <p>I have a simple experiment with 4 options and I have some data of users using it. Each user is in one of 4 options (for the sake of argument we can call the first on the control). Each user either was a success or failure, so I know the conversion rate of each option. How do I figure out if my test is statisically significant, or if it's all just random.</p>
  
  <p>Essentially my input will be <code>[ (a_yes, a_total), (b_yes, b_total), (c_yes, c_total), (d_yes, d_total)]</code>, and it's easy to figure out the conversion rate for each option, even how well each one does compared to a, but how do I figure out the statistical significance of it so I can know if this test is telling me anything, or if I should continue to let it run and collect more data.</p>
  
  <p>I've seen some guides on using some formulæ when there are only 2 options (the traditional a/b test), but I want this library to handle multiple options. Is it even possible to calculate statistical significance with multiple values.</p>
  
  <p>Essentially, I'm trying to do something similar to this <a href="http://mixpanel.com/labs/split-test-calculator" rel="nofollow">http://mixpanel.com/labs/split-test-calculator</a> but in python. Bonus points if there's some python library I can just "pip install …"</p>