Hypothesis Test for Difference of Means Hypothesis Test for Difference of Means
Hypothesis Test for Difference of Means
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- In the last video, we came up with a 95% confidence interval
- for the mean weight loss between the low-fat group and
- the control group.
- In this video, I actually want to do a hypothesis test,
- really to test if this data makes us believe that the
- low-fat diet actually does anything at all.
- And to do that let's set up our null and alternative
- So our null hypothesis should be that this
- low-fat diet does nothing.
- And if the low-fat diet does nothing, that means that the
- population mean on our low-fat diet minus the population mean
- on our control should be equal to zero.
- And this is a completely equivalent statement to saying
- that the mean of the sampling distribution of our low-fat
- diet minus the mean of the sampling distribution of our
- control should be equal to zero.
- And that's because we've seen this multiple times.
- The mean of your sampling distribution is going to be
- the same thing as your population mean.
- So this is the same thing is that.
- That is the same thing is that.
- Or, another way of saying it is, if we think about the mean
- of the distribution of the difference of the sample
- means, and we focused on this in the last video, that that
- should be equal to zero.
- Because this thing right over here is the same thing as that
- right over there.
- So that is our null hypothesis.
- And our alternative hypothesis,
- I'll write over here.
- It's just that it actually does do something.
- And let's say that it actually has an improvement.
- So that would mean that we have more weight loss.
- So if we have the mean of Group One, the population mean
- of Group One minus the population mean of Group Two
- should be greater then zero.
- So this is going to be a one tailed distribution.
- Or another way we can view it, is that the mean of the
- difference of the distributions, x1 minus x2 is
- going to be greater then zero.
- These are equivalent statements.
- Because we know that this is the same thing as this, which
- is the same thing as this, which is what I
- wrote right over here.
- Now, to do any type of hypothesis test, we have to
- decide on a level of significance.
- What we're going to do is, we're going to assume that our
- null hypothesis is correct.
- And then with that assumption that the null hypothesis is
- correct, we're going to see what is the probability of
- getting this sample data right over here.
- And if that probability is below some threshold, we will
- reject the null hypothesis in favor of the alternative
- Now, that probability threshold, and we've seen this
- before, is called the significance level, sometimes
- called alpha.
- And here, we're going to decide for a significance
- level of 95%.
- Or another way to think about it, assuming that the null
- hypothesis is correct, we want there to be no more than a 5%
- chance of getting this result here.
- Or no more than a 5% chance of incorrectly rejecting the null
- hypothesis when it is actually true.
- Or that would be a type one error.
- So if there's less than a 5% probability of this happening,
- we're going to reject the null hypothesis.
- Less than a 5% probability given the null hypothesis is
- true, then we're going to reject the null hypothesis in
- favor of the alternative.
- So let's think about this.
- So we have the null hypothesis.
- Let me draw a distribution over here.
- The null hypothesis says that the mean of the differences of
- the sampling distributions should be equal to zero.
- Now, in that situation, what is going to be our critical
- region here?
- Well, we need a result, so we're going to need some
- critical value here.
- Because this isn't a normalized normal
- But there's some critical value here.
- The hardest thing is statistics is getting the
- wording right.
- There's some critical value here that the probability of
- getting a sample from this distribution above that value
- is only 5%.
- So we just need to figure out what this critical value is.
- And if our value is larger than that critical value, then
- we can reject the null hypothesis.
- Because that means the probability of getting this is
- less than 5%.
- We could reject the null hypothesis and go with the
- alternative hypothesis.
- Remember, once again, we can use Z-scores, and we can
- assume this is a normal distribution because our
- sample size is large for either of those samples.
- We have a sample size of 100.
- And to figure that out, the first step, if we just look at
- a normalized normal distribution like this, what
- is your critical Z value?
- We're getting a result above that Z value,
- only has a 5% chance.
- So this is actually cumulative.
- So this whole area right over here is
- going to be 95% chance.
- We can just look at the Z table.
- We're looking for 95% percent.
- We're looking at the one tailed case.
- So let's look for 95%.
- This is the closest thing.
- We want to err on the side of being a little bit maybe to
- the right of this.
- So let's say 95.05 is pretty good.
- So that's 1.65.
- So this critical Z value is equal to 1.65.
- Or another way to view it is, this distance right here is
- going to be 1.65 standard deviations.
- I know my writing is really small.
- I'm just saying the standard deviation of that
- So what is the standard deviation of that
- We actually calculated it in the last video, and I'll
- recalculate it here.
- The standard deviation of our distribution of the difference
- of the sample means is going to be equal to the square root
- of the variance of our first population.
- Now, the variance of our first population, we don't know it.
- But we could estimate it with our sample standard deviation.
- If you take your sample standard deviation, 4.67 and
- you square it, you get your sample variance.
- And so this is the variance.
- This is our best estimate of the variance of the
- And we want to divide that by the sample size.
- And then plus our best estimate of the variance of
- the population of group two, which is 4.04 squared.
- The sample standard deviation of group two squared.
- That gives us variance divided by 100.
- I did before in the last. Maybe it's still sitting on my
- Yes, it's still sitting on the calculator.
- It's this quantity right up here.
- 4.67 squared divided by 100 plus 4.04
- squared divided by 100.
- So it's 0.617.
- So this right here is going to be 0.617.
- So this distance right here, is going to
- be 1.65 times 0.617.
- So let's figure out what that is.
- So let's take 0.617 times 1.65.
- So it's 1.02.
- This distance right here is 1.02.
- So what this tells us is, if we assume that the diet
- actually does nothing, there's a only a 5% chance of having a
- difference between the means of these two samples to have a
- difference of more than 1.02.
- There's only a 5% chance of that.
- Well, the mean that we actually got is 1.91.
- So that's sitting out here someplace.
- So it definitely falls in this critical region.
- The probability of getting this, assuming that the null
- hypothesis is correct, is less than 5%.
- So it's smaller probability than our significance level.
- Actually, let me be very clear.
- The significance level, this alpha right
- here, needs to be 5%.
- Not the 95%.
- I think I might have said here.
- But I wrote down the wrong number there.
- I subtracted it from one by accident.
- Probably in my head.
- But anyway, the significance level is 5%.
- The probability given that the null hypothesis is true, the
- probability of getting the result that we got, the
- probability of getting that difference, is less than our
- significance level.
- It is less than 5%.
- So based on the rules that we set out for ourselves of
- having a significance level of 5%, we will reject the null
- hypothesis in favor of the alternative that the diet
- actually does make you lose more weight.
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