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(female narrator)
This is just a couple of tips

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on how to use the 
significance test flow chart.

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As you go through each problem, this
flow chart can help you to determine

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what type of significance test you 
should perform on that problem.

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The first thing you want to do is you
want to read the problem.

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When you're done reading the 
problem, you need to ask yourself,

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"What was the data collected from 
each member of the sample?"

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For instance, did you collect 
a weight, or a height,

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or did you take their temperature,

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or did you count the number 
of dogs that they had?

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That would be numerical data. 
When you read the problem,

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did you see some key words like mean,
or average, or standard deviation.

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All of those things indicate that you
have a quantitative data problem.

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On the flip side of that, were there some
keywords like proportion, percent, rates,

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or was each member of the 
sample asked a yes or no question,

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such as, "Have you had 
a heart attack? Yes or no?"

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Maybe they were asked, "Are you 
overweight? Yes or no?"

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"Do you support the 
president? Yes or no?"

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So that type of a thing. That 
would be qualitative data.

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Once you determine the type of data,

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then you follow the decision 
tree down that side.

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For instance, if you have quantitative
data, then you would ask yourself,

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"Do I have two populations or 
do I have one population?"

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Now remember, an easy way to tell
whether you have one or two populations

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is to look for the samples 
that are given in the problem.

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If you're only given one sample,
then you only have one population.

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If you're given two samples, 
then you have two populations.

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You have to be given the complete
information about both of those samples,

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like the sample mean and sample standard
deviation, for two separate samples

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in order to determine 
you have two populations.

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Once you determine how 
many populations that you have,

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if you have one population, you're going
to do the t-stat one sample.

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If you have two independent populations,

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then you're going to do the 
t-stat two samples.

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Keep in mind that you need to determine
whether you're going to pull the variances

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or not pull the variances.

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Down here, we have the little 
tip on how to decide that.

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It's just the ratio of the larger 
sample standard deviation

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divided by the smaller sample
standard deviation,

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and looking to see if it's greater
than two, then you remove

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the pulled variances check mark, 
and if it's less than two,

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then you keep the pulled 
variances check mark.

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Now remember if you have 
two dependent samples,

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the two samples are related to each other,
such as a before and an after,

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a pre and a post test. The two samples are
related because it's the same person

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taking the pre-test and the 
same person taking the post-test,

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or maybe you're testing 
two different kinds of tires,

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so you take a car and you 
drive it with the first tire,

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and then you take the same car and you
drive it with the second tire.

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Those two samples are related because
it's the same car driving both times.

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If they're dependent samples, 
then you have a choice

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of either finding the differences 
between the two samples

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and doing a one sample t-test, or under
t-stats you can do the paired t-test.

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If you have more than two 
populations, we only have one test

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where we had more than two populations
and that was from chapter 14,

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and that was where we had three, four,
five, six, as many populations

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as were needed, and that was to perform
the one-way ANOVA test.

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Now, if you have two quantitative
variables, like you were collecting

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the height of a person and 
the weight of a person,

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and you were asked to decide if the 
variables were dependent,

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or associated, or independent, if 
you saw those words in there,

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but you have two quantitative 
variables, a height and the weight,

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then that would be a 
regression type problem,

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and you would perform that significance 
test using the test on beta, the slope,

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using the regression simple linear
command in StatCrunch,

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so again that's for two 
quantitative variables,

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and you're going to look for phrases
like "Determine if the variables

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are dependent, or 
associated, or independent"

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Back over here on the qualitative 
side, it's a little bit shorter

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on the qualitative side, but starting out
the same way we did on the quantitative

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side, we need to determine if we 
have one or two populations.

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Again, if you have two populations then
you're going to have information

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for two samples. You're going to be 
given the number of successes

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and the total sample size 
for two different samples.

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If you do determine that 
you have one population,

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then you would do a 
proportion stat one sample.

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If you do determine that you 
have two populations,

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then again you're going 
to do a proportion stat,

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but you're going to do two samples.

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Our last test for qualitative data,
is if we have two variables.

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For examples, we might be looking at
gender and happiness.

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That would be two qualitative variables.
For gender, you would be male or female.

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For happiness, they might indicate very
happy, or happy, and not happy,

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so those are word answers to those.
"How happy are you?"

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Very happy, happy, not happy.

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Those are word answers so those are
definitely qualitative variables,

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and it was two variables. 
We were collecting gender

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and we were collecting level of happiness
so we have two qualitative variables.

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In the problem you can also look to see
if it says the variables are dependent,

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associated, or independent.

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Also note, a little hint here, that when
you do have two qualitative variables,

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it's typically the data will be 
shown in a contingency table,

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so all of these clues here 
help you determine

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that you're going to perform a 
chi-square independence test.

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So this is a little bit about how 
to use this decision tree,

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or significance test flow chart. 
Sometimes I call it a decision tree

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because it branches off,

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or sometimes I just call it a 
significance test flow chart.

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But anyway, this is a little bit about
how to use this document.