(female narrator) This is just a couple of tips
on how to use the significance test flow chart.
As you go through each problem, this flow chart can help you to determine
what type of significance test you should perform on that problem.
The first thing you want to do is you want to read the problem.
When you're done reading the problem, you need to ask yourself,
"What was the data collected from each member of the sample?"
For instance, did you collect a weight, or a height,
or did you take their temperature,
or did you count the number of dogs that they had?
That would be numerical data. When you read the problem,
did you see some key words like mean, or average, or standard deviation.
All of those things indicate that you have a quantitative data problem.
On the flip side of that, were there some keywords like proportion, percent, rates,
or was each member of the sample asked a yes or no question,
such as, "Have you had a heart attack? Yes or no?"
Maybe they were asked, "Are you overweight? Yes or no?"
"Do you support the president? Yes or no?"
So that type of a thing. That would be qualitative data.
Once you determine the type of data,
then you follow the decision tree down that side.
For instance, if you have quantitative data, then you would ask yourself,
"Do I have two populations or do I have one population?"
Now remember, an easy way to tell whether you have one or two populations
is to look for the samples that are given in the problem.
If you're only given one sample, then you only have one population.
If you're given two samples, then you have two populations.
You have to be given the complete information about both of those samples,
like the sample mean and sample standard deviation, for two separate samples
in order to determine you have two populations.
Once you determine how many populations that you have,
if you have one population, you're going to do the t-stat one sample.
If you have two independent populations,
then you're going to do the t-stat two samples.
Keep in mind that you need to determine whether you're going to pull the variances
or not pull the variances.
Down here, we have the little tip on how to decide that.
It's just the ratio of the larger sample standard deviation
divided by the smaller sample standard deviation,
and looking to see if it's greater than two, then you remove
the pulled variances check mark, and if it's less than two,
then you keep the pulled variances check mark.
Now remember if you have two dependent samples,
the two samples are related to each other, such as a before and an after,
a pre and a post test. The two samples are related because it's the same person
taking the pre-test and the same person taking the post-test,
or maybe you're testing two different kinds of tires,
so you take a car and you drive it with the first tire,
and then you take the same car and you drive it with the second tire.
Those two samples are related because it's the same car driving both times.
If they're dependent samples, then you have a choice
of either finding the differences between the two samples
and doing a one sample t-test, or under t-stats you can do the paired t-test.
If you have more than two populations, we only have one test
where we had more than two populations and that was from chapter 14,
and that was where we had three, four, five, six, as many populations
as were needed, and that was to perform the one-way ANOVA test.
Now, if you have two quantitative variables, like you were collecting
the height of a person and the weight of a person,
and you were asked to decide if the variables were dependent,
or associated, or independent, if you saw those words in there,
but you have two quantitative variables, a height and the weight,
then that would be a regression type problem,
and you would perform that significance test using the test on beta, the slope,
using the regression simple linear command in StatCrunch,
so again that's for two quantitative variables,
and you're going to look for phrases like "Determine if the variables
are dependent, or associated, or independent"
Back over here on the qualitative side, it's a little bit shorter
on the qualitative side, but starting out the same way we did on the quantitative
side, we need to determine if we have one or two populations.
Again, if you have two populations then you're going to have information
for two samples. You're going to be given the number of successes
and the total sample size for two different samples.
If you do determine that you have one population,
then you would do a proportion stat one sample.
If you do determine that you have two populations,
then again you're going to do a proportion stat,
but you're going to do two samples.
Our last test for qualitative data, is if we have two variables.
For examples, we might be looking at gender and happiness.
That would be two qualitative variables. For gender, you would be male or female.
For happiness, they might indicate very happy, or happy, and not happy,
so those are word answers to those. "How happy are you?"
Very happy, happy, not happy.
Those are word answers so those are definitely qualitative variables,
and it was two variables. We were collecting gender
and we were collecting level of happiness so we have two qualitative variables.
In the problem you can also look to see if it says the variables are dependent,
associated, or independent.
Also note, a little hint here, that when you do have two qualitative variables,
it's typically the data will be shown in a contingency table,
so all of these clues here help you determine
that you're going to perform a chi-square independence test.
So this is a little bit about how to use this decision tree,
or significance test flow chart. Sometimes I call it a decision tree
because it branches off,
or sometimes I just call it a significance test flow chart.
But anyway, this is a little bit about how to use this document.