## ← 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.