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← Statistical Rigor Exercise - Intro to Data Science

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Showing Revision 5 created 05/24/2016 by Udacity Robot.

  1. Alright. So, why are statistical significance tests useful? So,
  2. they do provide a formalized framework for comparing and
  3. evaluating data. And they do enable us to evaluate
  4. whether perceived effects in our data set reflect differences across
  5. the whole population. They do not make a bad
  6. result look good. Significant sets are useful because they provide
  7. a formalized framework for comparing and evaluating data. Different
  8. tests have different assumptions and rules that they incorporate, and
  9. using a particular test ensures that everyone is on the
  10. same page in so far as what we're assuming about
  11. our data. Significance tests also enable us to evaluate whether
  12. perceived effects in our data set reflect differences across the whole
  13. population. As was the case with our company, where ten
  14. out of ten people polled preferred the color blue. Sometimes an
  15. affect that we seen in a small sample does not
  16. reflect what might be true across the entire population. A statistical
  17. significance test let's us formally determine whether or not this might
  18. be the case. Unfortunately, a bad result is not going to
  19. look any better or worse as a result of using a
  20. statistical significance test. If our data's
  21. bad, or there's really no difference
  22. between our two samples. We're not going to be able to
  23. undo that with a test. It is possible though that different tests
  24. might give us different results. The really important thing and we'll
  25. go into this a bit more is that you need to use
  26. the right test in the right situations. Why don't we talk a
  27. little bit about how we might actually run a statistical significance test.