
Alright. So, why are statistical significance tests useful? So,

they do provide a formalized framework for comparing and

evaluating data. And they do enable us to evaluate

whether perceived effects in our data set reflect differences across

the whole population. They do not make a bad

result look good. Significant sets are useful because they provide

a formalized framework for comparing and evaluating data. Different

tests have different assumptions and rules that they incorporate, and

using a particular test ensures that everyone is on the

same page in so far as what we're assuming about

our data. Significance tests also enable us to evaluate whether

perceived effects in our data set reflect differences across the whole

population. As was the case with our company, where ten

out of ten people polled preferred the color blue. Sometimes an

affect that we seen in a small sample does not

reflect what might be true across the entire population. A statistical

significance test let's us formally determine whether or not this might

be the case. Unfortunately, a bad result is not going to

look any better or worse as a result of using a

statistical significance test. If our data's

bad, or there's really no difference

between our two samples. We're not going to be able to

undo that with a test. It is possible though that different tests

might give us different results. The really important thing and we'll

go into this a bit more is that you need to use

the right test in the right situations. Why don't we talk a

little bit about how we might actually run a statistical significance test.