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## Comparing two proportions

Current time:0:00Total duration:10:00

# Comparing population proportions 2

## Video transcript

Where we left off in the last
video, we were trying to figure out if there's a
meaningful difference between the proportion of men voting
for a candidate and the proportion of women. We sampled 1,000 men, sampled
1,000 women, and we got a sample proportion for
each of them. We got 0.642 for the men and
0.591 for the women. But our goal is to get a 95%
confidence interval. So just based on our actual
sample, we got-- let me write it over here. We got our sample proportion for
the men minus-- let me do this in a neutral color. We got our sample proportion for
the men minus our sample proportion for the women
being 0.642 minus 0.591, that's 0.051. I just subtracted
this from that. So what we want to do when we
want a confidence interval, we want to be confident. I'll always have to say that
because it's not going to be super precise. We want to be confident that
there's a 95% chance that this thing right here-- remember,
when we took the two sample proportions and took their
difference, it's like taking a sample from the sampling
distribution of the statistic. So we want a 95% chance that
the true mean or the true value of this, that P1 minus P2
is within some range, let's say is within d, I'll say d for
distance, of the actual difference that we got
with our samples. Within d of 0.051. And I write this multiple
times, but I always write it this way. I don't just give the
formula that you normally see in books. It's very easy to memorize if
you do, but this way, you actually see why
this confidence interval makes sense. If there's a 95% chance that P1
minus P2, the actual true proportions, the difference of
the true proportions, is within d of the difference
between our sample proportions, this statement
right here is the same thing that there's a 95% chance that
0.051 is within d of this actual parameter, P1 minus
P2, which is the same thing as the mean. So we need to figure out some
distance around this mean, where if we take a random sample
from this, and this is a random sample from this
distribution, it has a 95% chance of being within d of
this mean, because if it's within d of the mean, then
there's also a 95% chance that the mean is within d of our
sample, and then we'll have our confidence interval. Our confidence interval would be
this value plus d and this value minus d. So what are these? What is the distance d? Well, in a normalized normal
distribution, I got a Z-table right over here, and we can
assume everything is normal, especially the sampling
distributions because our n is so big and also our proportion
is not close to zero or one. It's nice and close to the
middle, so we don't end up with all these weird cases
near the edges. We say, OK, how do we contain
the middle 95%? How many standard deviations in
a normal distribution do we need to be away from the mean in
order to contain 95% of the probability? Now these Z-tables, and we've
done it multiple times, give you cumulative distribution. We're looking for this Z-value
right over here. If it's containing 95%, you're
going to have 2.5% over here and you're going to have
2.5% over here. So from a Z-table's point of
view, this Z-table gives you the cumulative probability
up to that Z-value. So what we're looking for
is actually 97.5%. We're looking for something
that contains all of this over here. If we get the Z-value and then
apply it on both sides, then we're going to have something
that contains 95%. So let's look up the 97.5. 97.5 is right over there, and
that is 1.96 standard deviations. So this is 1.96 for a normalized
standard deviation, or a Z-score of 1.96. So if we looked to this normal
distribution right over here, this distance that we care
about is going to be 1.96 times the standard deviation of
this distribution, so it's going to be 1.96 times
all of this business. 1.96 times the standard
deviation of this distribution. And so we just need to
calculate this and multiply it by 1.96. Now, we have a problem. We don't know the true
parameters P1 and P2. We don't know the true
population parameters. We don't know P1 and P2. That's part of the problem. We're trying to figure out
if there's a meaningful difference between P1 and P2. But we've seen it
multiple times. Since our sample size is a
large, we can estimate P1 and P2 with our sample
proportions. So we could change this to
approximately and we can use our sample proportions. And we know what those
values are. And actually this n over
here was 1,000. So let's figure that out. Let's just get the
calculator out. It's just going to be one
big calculation here. So what we have is the square
root, and then in parentheses, our sample proportion for the
men is 0.642, and then we're going to multiply that times
1 minus 0.642, close parentheses. That's that over there
divided by 1,000. And then we're going to add to
that plus-- do the same thing for the women. Our sample proportion is 0.591
times 1 minus 0.591. So that's this term right over
here divided by 1,000. Once again, I need to get
the parentheses right. And then we just need to close
the parentheses, this original parentheses, because we're
taking the square root of everything. So we get 0.021, or maybe
we'll say 0.022. So this value right here
is approximately 0.022. So going back to our question,
or this distance that we care about, this value is going to be
approximately, or our best estimate of it, is 0.022. So let's just multiply that. 0.022 times 1.96 gives 0.043. I'll just round it. So this right here is
equal to 0.043. And just like that, we have
our confidence interval. We know that there's a 95%
chance that the true difference of the proportions is
within 0.043 of the actual difference of our sample
proportions that we got. Or if we actually want to get
an interval, we take this value minus 0.043. So let's do that. So we could have 0.051
minus 0.043 is going to give us 0.008. And then if we add
it, so 0.051 plus 0.043, it gives us 0.094. So the 95% confidence interval
between the proportions of men and the proportion of women who
are going to vote for the candidate for P1 minus
P2 is 0.008 to 0.094. I have it right here
on the calculator. And we're done. So it does seem we're confident
that there's a 95% chance that men are more
likely to vote for the candidate than women.