- Sampling distribution of sample proportion part 1
- Sampling distribution of sample proportion part 2
- Normal conditions for sampling distributions of sample proportions
- The normal condition for sample proportions
- Mean and standard deviation of sample proportions
- Probability of sample proportions example
- Finding probabilities with sample proportions
- Sampling distribution of a sample proportion example
Conditions for roughly normal sampling distribution of sample proportions.
- [Instructor] What we're going to do in this video is think about under which conditions does the sampling distribution of the sample portions, in which situations does it look roughly normal, and under which situations does it look skewed right so does it look something like this, and under which situations does it look skewed left maybe something like that. And the conditions that we're going to talk about, and this is a rough rule of thumb, that if we take our sample size and we multiply it by the population proportion that we care about and that is greater than or equal to 10 and if we take the sample size and we multiply it times one minus the population proportion and that also is greater than or equal to 10, if both of these are true, the rule of thumb tells us that this is going to be approximately normal in shape, the sampling distribution of the sample proportions. So with that in our minds, let's do some examples here. So this first example says, Emiliana runs a restaurant that receives a shipment of 50 tangerines every day. According to the supplier, approximately 12% of the population of these tangerines is overripe. Suppose that Emiliana calculates the daily proportion of overripe tangerines in her sample of 50. We can assume the supplier's claim is true and that the tangerines each day represent a random sample. What will be the shape of the sampling distribution, what will be the shape of the sampling distribution of the daily proportions of overripe tangerines? Pause this video, think about what we just talked about and see if you can answer this. All right, so right over here, we're getting daily samples of 50 tangerines. So for this particular example, our n is equal to 50 and our population proportion, the proportion that is overripe is 12% so p is 0.12. So if we take n times p, what do we get? NP is equal to 50 times 0.12, well 100 times this would be 12 so 50 times this is going to be equal to six and this is less than or equal to 10. So this immediately violates this first condition and so we know that we're not going to be dealing with a normal distribution. And so the question is, how is it going to be skewed? And the key realization is remember, the mean of the sample proportions or the sampling distribution of the sample proportions or the mean of the sampling distribution of the daily proportions that that's going to be the same thing as our population proportion so the mean is going to be 12%. So if I were to draw it, let me see if I were to draw it right over here where this is 50% and this is 100%, our mean is gonna be right over here at 12% and so you're gonna have it really high over there and then it's gonna be skewed to the right. You're gonna have a big long tail. So this is going to be skewed to the right. Let's do another example. So here we're told, according to a Nielsen survey, radio reaches 88% of children each week. Suppose we took weekly random samples of n equals 125 children from this population and computed the proportion of children in each sample whom radio reaches. What will be the shape of the sampling distribution of the proportions of children the radio reaches? Once again, pause this video and see if you can figure it out. All right, well let's just figure out what n and p are. Our sample size here n is equal to 125 and our population proportion of the proportion of children that are reached each week by radio is 88% so p is 0.88. So now let's calculate np so n is 125 times p is 0.88 and is this going to be greater than or equal to 10. Well, we don't even have to calculate this exactly. This is almost 90% of 125. This is actually going to be over 100 so it for sure is going to be greater than 10 so we meet this first condition. But what about the second condition? We could take n 125 times one minus p so this is times 0.12 so this is 12% of 125. Well, even 10% of 125 would be 12.5 so 12% is for sure going to be greater than that so this too is going to be greater than 10. I didn't even have to calculate it. I could just estimate it and so we meet that second condition. So even though our population proportion is quite high, it's quite close to one here, because our sample size is so large, it still will be roughly normal and one way to get the intuition for that is so this is a proportion of zero, let's say this is 50% and this is 100%, so our mean right over here is gonna be 0.88 for our sampling distribution of the sample proportions. If we had a low sample size, then our standard deviation would be quite large and so then you would end up with a left skewed distribution. But we saw before the higher your sample size, the smaller your standard deviation for the sampling distribution and so what that does is it tightens up, it tightens up the standard deviation and so it's going to look more normal. It's gonna look closer to being normal. So we'll say approximately normal because it met our conditions for this rule of thumb. Is it gonna be perfectly normal? No. In fact, if we didn't have this rule of thumb to draw the line, some might even argue that we still have a longer tail to the left than we do to the right, maybe it's skewed to the left, but using this threshold, using this rule of thumb which is the standard in statistics, this would be viewed as approximately normal.