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### Course: Statistics and probability > Unit 11

Lesson 4: More confidence interval videos# T-statistic confidence interval

Sal computes a confidence interval for the emission from an engine with a new design. Created by Sal Khan.

## Want to join the conversation?

- Hello Sal. I checked up a t-distribution table and found that the degrees of freedom went upto 120. Why would we need that much when we only use the t-distribution when n < 30?(21 votes)
- when we use sigma and s?(3 votes)
- sigma is the standard deviation of a population, and s is the standard deviation of a sample. My tip for remembering it is that the population is unknown and mysterious but the sample is very clear data, so that's why we use mysterious Greek letters like mu and sigma to describe population statistics but familiar Latin letters like x-bar and s to describe sample statistics.(37 votes)

- I may have missed this somewhere and a site search didn't seem to find it: where might the t statistic videos be? Thanks.(5 votes)
- couldn't find any video specifically describing this way to do a t statistic too. But I guess he means the videos about the t statistic in general, like "Introduction to t statistics" and stuff.

Since the formula is basically the same, just written in another way. So the formula we were given in the videos is:

x_bar +- t* sigma/root(n) to get your confidence interval

using this you can conclude that:

x_bar - t* sigma/root(n) < mu < x_bar + t* sigma/root(n)

all - x_bar

=> -t* sigma/root(n) < mu - x_bar < t* sigma/root(n)

all /sigma/root(n):

=> -t* < (mu - x_bar)/(sigma/root(n)) < t*

all /(-1)

=> t* > (x_bar - mu)/(sigma/root(n)) > -t*

<=> -t* < (x_bar - mu)/(sigma/root(n)) < t*

And here you have the formula he used in this video(11 votes)

- sort of like Katoriak's question. Why do you use the degrees of freedom for anything? I'm not making an intuitive connection.

Mattson's answer makes sense...but why do we replace 'n' with the dof ?(2 votes)- You use (n-1) degrees of freedom because all the values leading up to that last value can be any value, but the last one must fit in just right to make everything before it match the value on the other side of the equal sign. Let's say I have one hundred toys. Furthermore, I have 10 buckets. I only have (10 buckets- 1 bucket= 9 buckets) 9 buckets where I can store these toys. Whether those buckets have equal amounts of toys or not, the last bucket must bring the total number of toys to 100. So I can put ten toys per bucket (10 toys per 10 buckets equals 100), or 99 toys in the first bucket but zero toys in the middle buckets, but the last bucket must have 1, because 99 toys+1 toy= 100 toys.(11 votes)

- Around the end of the video, Sal talks about how there's a 95% chance that it's true that our real population mean is between 19.3 and 15.04. I don't want to confuse anyone but what I learnt in class is that it rather means that a 95% confidence interval represents the fact that when sampling from the population 95% of the time we're going to get a mean between those two values.

It relates more to sampling a certain amount of individuals from a population multiple times and getting different sample means which could all be right.

Its hard to explain, and a small distinction but could be important when writing a report.

Or am I mistaken?(7 votes)- My understanding is both of the two things you're saying are true. If there's a 95% chance I'm standing within 2.13 units from you, then there's a 95% chance you're standing within 2.13 units from me.(2 votes)

- so my teacher always told us we want to "reject the null hypothesis" and if we can't we have to state that we "could not reject the null hypothesis". why was that?(3 votes)
- It's because we only have enough evidence to show that the null hypothesis is true or not, not that the alternative is true. The alternative might not be true, but we don't have enough evidence to show that it is true.(10 votes)

- In the beginning of the video Sal refers to another video with the same problem. Where can I find that video? Thanks.(4 votes)
- Here is the video you're looking for: https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/more-significance-testing-videos/v/small-sample-hypothesis-test

Hope this helps!(4 votes)

- wait, how'd you get S to be 2.98?(5 votes)
- He says it's from the previous video. In the previous video, he says he calculated it to be 2.98.

If you do the math yourself, you'll get the same value. Just make sure you divide by 9, not 10 in the last step to find the variance.(1 vote)

- at4:32he explain what t* is equal to. he says we've seen this multiple times, but i don't remember this explained before.

would it be this?

t*=(x_bar-mu)/s/sqrt(n)

It seems very similar to the z score, but instead of dividing by the sample standard deviation of the sampling distribution sigma/sqrt(n) it uses s/sqrt(n)

it this explained sonewhere else?(3 votes)- This formula is for the t-score,
*not*the critical t value (or t*). The t-score indicates how many standard deviations a certain value is from the mean in a t-distribution, similar to what the z-score indicates for a z-distribution.

Hope this clears things up!😄(2 votes)

- from where did he pullout the formula (17.17-mean)/2.98/sqrt10(3 votes)
- From the definition of the t statistic. 17.17 is the sample mean. 2.98/sqrt(10) is the standard error of the sample mean.(1 vote)

## Video transcript

This is the same problem that
we had in the last video. But instead of trying to figure
out whether the data supplies sufficient evidence to
conclude that the engines meet the actual emissions
requirement, and all of the hypothesis testing, I thought I
would also use the same data that we had in the last video to
actually come up with a 95% confidence interval. So you could ignore the
question right here. You can ignore all of this. I'm just using that same data
to come up with a 95% confidence interval for the
actual mean emission for this new engine design. So we want to find a 95%
confidence interval. And as you could imagine,
because we only have 10 samples right here, we're
going to want to use a T-distribution. And right down here
I have a T-table. And we want a 95% confidence
interval. So we want to think about the
range of T-values that 95-- or the range that 95% of T-values
will fall under. So let's think about this way. So let me draw a T-distribution right over here. So a T-distribution looks
very similar to a normal distribution but it
has fatter tails. This end and this end will be
fatter than in a normal distribution. And then we want to find an
interval, so if this is a normalized T-distribution the
mean is going to be 0. And we want to find interval
of T-values between some negative value here and some
positive value here that contains 95% of the
probability. So this right here
has to be 95%. And to figure what these
critical T-values are at this end and this end, we can
just use a T-table. And we're going to use the
two-sided version of this because we're symmetric
around the center. So you look at the two-sided,
we want a 95% confidence interval, so we're going to
look right over here, 95% confidence interval. We have 10 data points,
which means we have 9 degrees of freedom. So 9 degrees of freedom for
our 10 data points. We just took 10 minus 1. So if we look over here, so for
a T-distribution with 9 degrees of freedom, you're
going to have 95% of the probability is going to be
contained within a T-value of-- so the T-value is going
to be between negative, so this value right here is 2.262,
and this value right here is negative 2.262. That's what this right
here tells us. That if you contain all the
values that are less than 2.262 away from the center of
your T-distribution, you will contain 95% of the
probability. So that is our T-distribution
right there. Let me make it very clear. This is our T-distribution. So if you randomly pick
a T-value from this T-distribution, it has a 95%
chance of being within this far from the mean. Or maybe we should
write this way. If I pick a random T-value, if
I take a random T-statistic-- let me write it this way--
there's a 95% chance that a random T-statistic is going
to be less than 2.262, and greater than negative 2.262. 95% percent chance. Now when we took this sample, we
could also derive a random T-statistic from this. We have our sample mean and our
sample standard deviation, our sample mean here is 17.17-- figured that out in the
last video, just add these up, divide by 10-- and
our sample standard deviation here is 2.98. So the T-statistic that we can
derive from this information right over here-- so let me
write it over here-- the T-statistic that we could derive
from this, and you can view this T-statistic as being
a random sample from a T-distribution. A T-distribution with 9
degrees of freedom. So the T-statistic that we
could derive from that is going to be our mean, 17.17
minus the true mean of our population. Or actually you would say the
true mean of our sampling distribution, which is also
going to be the same as the true mean of our population,
because that's our population mean over there, divided by s,
which is 2.98 over the square root of our number of samples. We've seen this multiple
times. This right here is
the T-statistic. So by taking this sample you
can say that we've randomly sampled a T-statistic from
this 9 degree of freedom T-distribution. So there's a 95% chance that
this thing right over here is going to be between-- is going
to be less than 2.262 and greater than negative 2.262. So the 95% probability still
applies to this right here. Now we just have to do some
math, calculate these things. So let me get my
calculator out. And so let me just
calculate this denominator right over here. So we have 2.98 divided by
the square root of 10. So that's 0.9423. So what I'm going to do is I'm
going to multiply both sides of this equation by this
expression right over here. So if I do that-- so let me just
do that right over-- so if I multiply this entire-- this
is really two equations or two inequalities
I should say. That this quantity is greater
than this quantity and that this quantity's greater
than that quantity. But we can operate on all of
them at the same time, this entire inequality. So what we want to do is
multiply this entire inequality by this value
right over here. And we just calculated it at
that value-- let me write it over here-- that 2.98-- I'll
write it right over here-- 2.98 over the square root
of 10 is equal to 0.942. So if I multiplied this entire
inequality by 0.942 I get, on this left-hand side over here
I have negative 2.262 times 0.942-- and it's a positive
number that we're multiplying the whole inequality by, so the
inequality signs are still going to be in the same
direction-- is less than-- we're multiplying this whole
expression by the same expression in the denominator
so it'll cancel out. So we're just going to be less
than 17.17 minus our population mean, which is going
to be less than 2.262 times, once again, 0.942. Let me scroll over to the
right a little bit. 0.942. Just be clear, I'm just
multiplying all three sides of this inequality by this number
right over here. In the middle this
cancels out. So if I multiply-- I'll just
write it over here-- 0.942, 0.942, 0.942. This and this is the same number
so that's why those cancel out. And now let's get the calculator
to figure out what these numbers are. So if we have the 0.942
times 2.262. So we're going to say
times 2.262 is 2.13. So this number right
over here on the right-hand side is 2.13. This number on the left is just
the negative of that. So it's negative 2.13. And then we still have our
inequalities-- is going to be less than 17.17 minus the mean,
which is less than 2.13. Now what I want to do is
I actually want to solve for this mean. And I don't like that negative
sign in the mean. I'd rather have this
swapped around. I'd rather have the
mean minus 17.17. So what I'm going to do is
multiply this entire inequality by negative 1. If you do that, if you multiply
the entire thing times negative 1, this quantity
right here, this negative 2.13 will become
a positive 2.13. But since we are multiplying
an inequality by a negative number you have to swap
the inequality sign. So this less than will become
a greater than. This negative mu will become
a positive mu. This positive 17.17 will become
a negative 17.17. We're going to have to swap this
inequality sign as well, and this positive 2.13 will
become a negative 2.13. And we're almost there. We just want to solve for mu. Have this inequality expressed
in terms of mu. So what we can do is now just
add 17.17 to all three sides of this inequality, and we are
left with 2.13 plus 17.17 is greater than mu minus 17.17 plus
17.17 is just going to be mu, which is greater than-- so
this is greater than mu, which is greater than negative
2.13 plus 17.17. Or a more natural way to write
it since we actually have a bunch of greater than signs,
that this is actually the largest number and this-- oh
sorry, this is actually the smallest number and this over
here is actually the largest number, is actually flipped--
you can just re-write this inequality the other way. So now we can write-- actually
let's just figure out what these values are. So we have 2.13 plus 17.17. So that is the high
end of our range. So that is 19.3. So this value right over here,
so this is 19-- let me do it in that same color-- this value
right here is 19.3 is going to be greater than mu,
which is going to be greater than-- and this is negative
2.13 plus 17.17. Or we could have 17.17 minus
2.13, which gives us 15.04. And remember, the whole thing,
all of this, we started with, there was a 95% chance that a
random T-statistic will fall in this interval. We had a random T-statistic,
and all we did is a bunch of math. So there's a 95% chance that any
of these steps are true. So there's a 95% chance
that this is true. There's a 95% chance that the
true population mean, which is the same thing as the mean of
the sampling distribution of the sample mean, there's a 95%
chance, or that we are confident that there's a 95%
chance, that it will fall in this interval. And we're done.