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- [Instructor] Fay read an article that said
26% of Americans can speak more than one language.
She was curious if this figure was higher in her city,
so she tested her null hypothesis
that the proportion in her city
is the same as all Americans, 26%.
Her alternative hypothesis is it's actually
greater than 26%,
where P represents the proportion of people in her city
that can speak more than one language.
She found that 40 of 120 people sampled
could speak more than one language.
So what's going on is here's the population of her city,
she took a sample,
her sample size is 120.
And then she calculates her sample proportion
which is 40 out of 120 and this is going
to be equal to one-third,
which is approximately equal to 0.33.
And then she calculates the test statistic
for these results was Z is approximately equal to 1.83.
We do this in other videos,
but just as a reminder of how she gets this,
she's really trying to say well how many
standard deviations above the assumed proportion,
remember when we're doing these significance tests
we're assuming that the null hypothesis is true
and then we figure out well what's the probability
of getting something at least this extreme
or this extreme or more?
And then if it's below a threshold,
then we would reject the null hypothesis
which would suggest the alternative.
But that's what this Z statistic is,
is how many standard deviations above
the assumed proportion is that?
So the Z statistic,
and we did this in previous videos,
you would find the difference between this,
what we got for our sample,
our sample proportion,
and the assumed true proportion.
So 0.33 minus 0.26,
all of that over the standard deviation
of the sampling distribution of the sample proportions.
And we've seen that in previous videos.
That is just going to be the assumed proportion,
so it would be just this.
It would be the assumed population proportion times one,
minus the assumed population proportion over N.
In this particular situation,
that would be 0.26 times one,
minus 0.26,
all of that over our N,
that's our sample size, 120.
And if you calculate this,
this should give us approximately 1.83.
So they did all of that for us.
And they say assuming that
the necessary conditions are met,
they're talking about the necessary conditions
to assume that the sampling distribution
of the sample proportions is roughly normal
and that's the random condition,
the normal condition,
the independence condition
that we have talk about in the past.
What is the approximate P value?
Well this P value,
this is the P value would be equal to
the probability of in a normal distribution,
we're assuming that the sampling distribution is normal
'cause we met the necessary conditions,
so in a normal distribution,
what is the probability of getting a Z
greater than or equal to 1.83?
So to help us visualize this,
let's visualize what the sampling
distribution would look like.
We're assuming it is roughly normal.
The mean of the sampling distribution right over here
would be the assumed population proportion,
so that would be P not.
When we put that little zero there
that means the assumed population
proportion from the null hypothesis,
and that's 0.26,
and this result that we got from our sample
is 1.83 standard deviations above
the mean of the sampling distribution.
So 1.83.
So that would be 1.83 standard deviations.
And so what we wanna do,
this probability is this area under
our normal curve right here.
So now let's get our Z table.
So notice this Z table gives us
the area to the left of a certain Z value.
We wanted it to the right of a certain Z value.
But a normal distribution is symmetric.
So instead of saying anything greater than or equal to
1.83 standard deviations above the mean,
we could say anything less than or equal to
1.83 standard deviations below the means.
So this is negative 1.83.
And so we could look at that
on this Z table right over here,
negative 1.8,
negative 1.83 is this right over here.
So 0.0336.
So there we have it.
So this is approximately 0.0336
or a little over 3% or a little less than 4%.
And so what Fay would then do is compare that
to the significance level that she should
have set before conducting this significance test.
And so if her significance level was say 5%,
well then that situation since this is lower
that that significance level,
she would be able to reject the null hypothesis.
She would say hey the probability
of getting this result assuming
that the null hypothesis is true,
is below my threshold.
It's quite low.
And so I will reject it
and it would suggest the alternative.
However, if her significance level was lower
than this for whatever reason,
if she has say a 1% significance level,
then she would fail to reject the null hypothesis.
