## Unit 7 Ap Classroom Progress Check Frq A

A video for unit 7, app classroom, um, FRQ progress, check a so um you're, given this. Uh, this idea here, first, the no calculator is allowed for th

- 18-Apr-2022

What we are going to do in this video is talk about the idea of power when we are dealing with significance tests and power is an idea that you might encounter in a first-year. Statistics course, it turns out that it's fairly difficult to calculate, but it's interesting to know what it means. And what are the levers that might increase the power or decrease the power in a significance test? So just to cut to the chase power is a probability.

You can view it as the probability that you're doing the. Right thing when the null hypothesis is not true. And the right thing is, you should reject the null hypothesis if it's not true. So it's, the probability of rejecting your null hypothesis, given that the null hypothesis is false. So you could view it as a conditional probability like that, but there are other ways to conceptualize it.

We can connect it to type 2 errors. For example, you could say this is equal to 1 minus the probability of not rejecting 1, minus the probability of not rejecting. Not rejecting the null hypothesis given that the null hypothesis is false. And this thing that I just described not rejecting the null hypothesis, given the null hypothesis is false.

This is that's the definition of a type 2 error. So you could view it as just the probability of not making a type 2 error or 1 minus the probability of making a type 2 error, hopefully that's, not confusing. So let me just write it the other way. So you could say, it's the probability of not making a type.

Type 2 error. So what are the things that would actually drive power and to help us conceptualize that I'll draw two sampling distributions one, if we assume that the null hypothesis is true and one where we assume that the null hypothesis is false. And the true population parameter something different from the null hypothesis is saying. So for example, let's say that we have a null hypothesis that our population mean is equal to let's just call it mu 1.

And we have an alternative hypothesis. So H sub. A that says, hey know, the population mean is not equal to mu 1. So if you assumed a world where the null hypothesis is true, so I'll do that in blue. So if we assume the null hypothesis is true, what would be our sampling distribution?

Remember, what we do in significance tests is we have some form of a population. Let me draw that you have a population over here. And our hypotheses are making some statement about a parameter in that population.

And to test it. We take a sample of a certain size we. Calculate a statistic in this case, we would be the sample mean.

And we say, if we assume that our null hypothesis is true, what is the probability of getting that sample statistic? And if that's below a threshold, which we call a significance level, we reject the null hypothesis. And so that world that we have been living in one way to think about it in a world, where you assume the null hypothesis is true. You might have a sampling distribution that looks something like this. The null hypothesis is true.

Then the center of your sampling distribution would be over here at mu1 and given your sample size, you would get a certain sampling distribution for the sample means if your sample size increases, this will be narrower if it decreases this thing is going to be wider. And you set a significance level, which is essentially your probability of rejecting the null hypothesis. Even if it is true, you could even view it as, and we've talked about it. You can view your significance level as a probability. Of making a type 1 error. So your significance level is some area and so let's say, it's, this area that I'm shading in orange over here that would be your significance level. So if you took sample over here, and you calculated its sample mean, and you happen to fall in this area or this area or this area over here, then you would reject your null hypothesis.

Now, if the null hypothesis actually was true, you would be committing a type 1 error without knowing about it. But for power, we. Are concerned with a type 2 error. So in this one it's, a conditional probability that our null hypothesis is false and so let's construct another sampling distribution. In the case where our null hypothesis is false. So let me just continue this line over here, and I'll do that so let's, imagine a world where our null hypothesis is false, and it's, actually the case that our mean is mu 2 and say, let's say that mu 2 is over here. And in this reality, our sampling distribution might look.

Something like this for once again, it'll be for a given sample size the larger, the sample size the narrower, this bell curve would be. And so it might look something like this. So in which situation so in this world, we should be rejecting the null hypothesis, but what are the samples?

In which case we are not rejecting the null hypothesis, even though we should well we're not going to reject the null hypothesis if we get samples. And if we get a sample here or a sample here or a sample here, a sample. Where if you assume the null hypothesis is true, the probability isn't that unlikely and so the probability of making a type 2 error when we should reject the null hypothesis. But we don't is actually this area over here.

And the power, the probability of rejecting the null hypothesis, given that it's false. So given that it's false would be this red distribution that would be the rest of this area over here. So how can we increase the power? Well, one way is to increase our alpha increase. Our significance level, if we increase our significance level say from that remember significance level is an area. So if we want it to go up if we increase the area, and it looked something like that now by expanding that significance area, we have increased the power because now this yellow area is larger.

We've pushed this boundary to the left of it. Now you might say, oh, well, hey, if we want to increase the power sounds like a good thing. Why don't we just always increase alpha? Well, the problem. With that is if you increase alpha, so let me write this down. So if you take alpha your significance level, and you increase it that will increase the power that will increase the power, but it's also going to increase your probability of a type 1 error, because remember that's what one way to conceptualize.

What alpha is what your significance level is it's, a probability of a type 1 error. Now what are other ways to increase your power? Well, if you increase your sample size, then both of these. Distributions will these sampling distributions are going to get narrower. And so if these sampling distributions, if both of these sampling distributions get narrower, then that situation where you are not rejecting your null hypothesis, even though you should is going to have a lot less area there's going to be one way to think about it there's going to be a lot less overlap between these two sampling distributions.

And so let me write that down. So another way is to if you increase n, your sample. Size that's going to increase your power. And this in general is always a good thing if you can do it.

Now, other things that may or may not be under your control is well. The less variability there is in the data set that would also make these sampling distributions narrower. And that would also increase the power so less variability. And you can measure that as by variance or standard deviation of your underlying data set that would increase your power.

Another thing that would increase the power is. If the true parameter is further, then what the null hypothesis is saying. So if you say true parameter far from null hypothesis, what it's saying that also will increase the power.

So these two are not typically under your control. But the sample size is, and the significance level is significance level, there's a trade-off, though, if you increase the power through that you're also increasing the probability of a type 1 error. So for a lot of researchers, they might say, hey, if a type 2 error is worse, I'm. Willing to make this trade-off, I'll increase the significance level.

But if a type 1 error is actually what I'm afraid of that I wouldn't want to use this lever. But in any case, increasing your sample size, if you can do, it is going to be a good thing.

A video for unit 7, app classroom, um, FRQ progress, check a so um you're, given this. Uh, this idea here, first, the no calculator is allowed for th

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