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Statistical population

From Wikipedia, the free encyclopedia

In statistics, a population is a set of similar items or events which is of interest for some question or experiment.[1] A statistical population can be a group of existing objects (e.g. the set of all stars within the Milky Way galaxy) or a hypothetical and potentially infinite group of objects conceived as a generalization from experience (e.g. the set of all possible hands in a game of poker).[2] A common aim of statistical analysis is to produce information about some chosen population.[3]

In statistical inference, a subset of the population (a statistical sample) is chosen to represent the population in a statistical analysis.[4] The ratio of the size of this statistical sample to the size of the population is called a sampling fraction. It is then possible to estimate the population parameters using the appropriate sample statistics.

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  • ✪ The Normal Distribution: Crash Course Statistics #19
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  • ✪ 2015 Video Game Trends and Statistics
  • ✪ Statistics - Find the z score
  • ✪ What Is Statistics: Crash Course Statistics #1


Hi, I’m Adriene Hill, and Welcome back to Crash Course Statistics. This is the episode you’ve been waiting for. The episode we designed this shelf for. The episode that you have heard a lot about. (NORMAL DIST MONTAGE) Well, today, we’ll get to see why we talk SO MUCH about the normal distribution. INTRO Things like height, IQ, standardized test scores, and a lot of mechanically generated things like the weight of cereal boxes are normally distributed, but many other interesting things from blood pressure, to debt, to fuel efficiency just aren’t. One reason we talk so much about normal distributions is because distributions of means are normally distributed, even if populations aren’t. The normal distribution is symmetric, which means its mean, median and mode are all the same value. And it’s most popular values are in the middle, with skinny tails to either side. In general, when we ask scientific questions, we’re not comparing individual scores or values like the weight of one blue jay, or the number of kills from one League of Legends game, we’re comparing groups--or samples--of them. So we’re often concerned with the distributions of the means, not the population. In order to meaningfully compare whether two means are different, we need to know something about their distribution: the sampling distribution of sample means. Also called the sampling distribution for short. And before we go any further, I want to say that the distribution of sample means is not something we create, we don’t actually draw an infinite number of samples to plot and observe their means. This distribution, like most distributions, is a description of a process. Take income. Income is skewed….so we might think the distribution of all possible mean incomes would also be skewed. But they’re actually normally distributed. In the real population there are people that make a huge amount of money. Think Oprah, Jeff Bezos, and Bill Gates. But when we take the mean of a group of three randomly selected people, it becomes much less likely to see extreme mean incomes because in order to have an income that’s as high as Oprah’s, you’d need to randomly select 3 people with pretty high incomes, instead of just one. Since scientific questions usually ask us to compare groups rather than individuals, this makes our lives a lot easier, because instead of an infinite amount of different distributions to keep track of, we can just keep track of one: the normal distribution. The reason that sampling distributions are almost always normal is laid out in the Central Limit Theorem. The Central Limit Theorem states that the distribution of sample means for an independent, random variable, will get closer and closer to a normal distribution as the size of the sample gets bigger and bigger, even if the original population distribution isn’t normal itself. As we get further into inferential statistics and making models to describe our data, this will become more useful. Many inferential techniques in statistics rely on the assumption that the distribution of sample means is normal, and the Central Limit Theorem allows us to claim that they usually are. Let’s look at a simulation of the Central Limit Theorem in action. For our first example, imagine a discrete, uniform distribution. Like dice rolls. The distribution of values for a single dice roll looks like this: With a sample size of 1--the regular distribution of dice values--there’s one way to get a 1, one way to get a 2, one way to get a 3….and so on. But we want to look at the mean of say...2 dice rolls, meaning our sample size is 2. With two dice. Let’s first look at all the sums of the dice rolls we can get: 2,3,4,5,6,7,8,9,10,11,12 There’s only one way to get 2 and 12, either two ones, or two 6’s, but there’s 6 ways to get 7, [1,6],[2,5], [3,4] or [6,1],[5,2], and [4,3]...which lends significance to the number 7 - which is the number you’ll roll most often. But back to means, we have the possible sums, but we want the mean, so we’ll divide each total value by two, giving us this distribution: Even though our population distribution is uniform, The distribution of sample means is looking more normal, even with a sample size of 2. As our sample size gets bigger and bigger, the middle values get more common, and the tail values are less and less common. We can use the multiplication rule from probability to see why that happens. If you roll a die one time, the probability of getting a 1--the lowest value--is ⅙. When you increase the number of rolls to two, the probability of getting a mean of 1, is now 1/36, or ⅙ times ⅙, since you have to get two 1’s to have a mean of 1. Getting a mean value of 2 is a little bit easier since you can have a mean roll of 2 both by rolling two 2’s, but also by rolling a 3 and a 1, or a 1 and a 3. So the probability is 3(1/36). If we had the patience to roll a die 20 times, the probability of getting a mean roll value of 1 would be (⅙)^20 since the only way to get a mean of 1 on 20 dice rolls is to roll a one. Every. Single. Time. So you can see that even with a sample size of only 20, the means of our dice rolls will look pretty close to normal. The mean of the distribution of sample means is 3.5, the same as the mean of our original uniform distribution of dice rolls, and this is always true about sampling distributions: Their mean is always the same as the population they’re derived from. So with large samples, the sample means will be a pretty good estimate of the true population mean. There are two separate distributions we’re talking about. There is the original population distribution that’s generating each individual die roll, and there is a distribution of sample means that tells you the frequency of all the possible sample means you could get by drawing a sample of a certain size--here 20--from that original population distribution. Again, population distribution. And sampling distribution of sample means. But while the mean of the distribution of sample means is the same as the population’s, it’s standard deviation is not, which might be intuitive since we saw how larger sample sizes render extreme values--like a mean roll value of 1 or 6--very unlikely, while making values close to the mean more and more likely. And it doesn’t just work for uniform population distributions. Normal population distributions also give normal distributions of sample means, as do skewed distributions, and this weird looking guy: In fact, with a large sample, any distribution with finite variance will have a distribution of sample means that is approximately normal. This is incredibly useful. We can use the nice, symmetric and mathematically pleasant normal distribution to calculate things like percentiles, as well as how weird or rare a difference between two sample means actually is. The standard deviation of a distribution of sample means is still related to the original standard deviation. But as we saw, the bigger the sample size, the closer your sample means are to the true population mean, so we need to adjust the original population standard deviation somehow to reflect this. The way we do it mathematically is to divide by the square root of n--our sample size. Since we divide by the square root of n, as n gets big, the standard deviation--or sigma--gets smaller.. which we can see in these simulations of sampling distributions of size 20, 50, and 100. The larger the sample size, the skinnier the distribution of sample means. For example, say you grab 5 boxes of strawberries at your local grocery store--you’re making the pies for a pie eating contest--and weigh them when you get home. The mean weight of a box of strawberries from your grocery store is 15oz. But that means that you don’t have quite enough strawberries. You thought that the boxes were about 16oz, and you wonder if the grocery store got a new supplier that gives you a little less. You do a quick Google search and find a small farming company’s blog. They package boxes of strawberries for a local grocery store, they list the mean weight of their boxes--16oz--and the standard deviation--1.25 oz. That’s all the information we need to calculate the distribution of sample means for a sample of 5 boxes. Part of the mathematical pleasantness of the normal distribution is that if you know the mean and standard deviation, you know the exact shape of the distribution. So you grab your computer and pull up a stats program to plot the distribution of sample means with a mean of 16oz and a standard deviation of 1.25 divided by the square root of 5--the sample size. We call The standard deviation of a sampling distribution the standard error so that we don’t get it confused with the population standard deviation, it’s still a standard deviation, just of a different distribution. Our distribution of sample means for a sample of 5 boxes looks like this. And now that we know what it looks like, we can see how different the mean strawberry box weights of 15oz really is. When we graph it over the distribution of sample means, we can see that it’s not too close to the mean of 16oz, but it’s not too far either...We need a more concrete way to decide whether the 15oz is really that far away from the mean of 16oz. It might help if we had a measure of how different we expect one sample mean to be from the true mean, luckily we do: the standard error which tells us the average distance between a sample mean and the true mean of 16oz. This is where personal judgement comes in. We could decide for example, that if a sample mean was more than 2 standard errors away from the mean, we’d be suspicious. If that was the case then maybe there was some systematic reduction in strawberries, because it’s unlikely our sample mean was randomly that different from the true mean. In this case our standard error would be 0.56. If we decided 2 standard errors was too far away, we wouldn’t have much to be suspicious about. Maybe we should hold off leaving a nasty comment on the strawberry farmers blog. Looking at the distribution of sample means helped us compare two means, but we can also use sampling distributions to compare other parameters like proportions, Regression Coefficients, or standard deviations, which also follow the Central Limit Theorem. The CLT allows us to use the same tools, like a distributions, with all different kinds of questions. You may be interested in whether your favorite baseball team has better batting averages, and your friend may care about whether Tylenol cures her headache faster than ibuprofen. Thanks to the CLT you can both use the same tools to find your answers. But when you look at things on a group level instead of the individual level, all these diverse shapes and the populations that make them converge to one common distribution: the normal distribution. And the simplicity of the normal distribution allows us to make meaningful comparisons between groups like whether hiring managers hire fewer single mothers, or whether male chefs make more money. These comparisons help us know where things fit in the world. Thanks for watching. I'll see you next time.



A subconcept of a population that shares one or more additional properties is called a subpopulation. For example, if the population is all Egyptian people, a subpopulation is all Egyptian males; if the population is all pharmacies in the world, a subpopulation is all pharmacies in Egypt. By contrast, a sample is a subset of a population that is not chosen to share any additional property.

Descriptive statistics may yield different results for different subpopulations. For instance, a particular medicine may have different effects on different subpopulations, and these effects may be obscured or dismissed if such special subpopulations are not identified and examined in isolation.

Similarly, one can often estimate parameters more accurately if one separates out subpopulations: the distribution of heights among people is better modeled by considering men and women as separate subpopulations, for instance.

Populations consisting of subpopulations can be modeled by mixture models, which combine the distributions within subpopulations into an overall population distribution. Even if subpopulations are well-modeled by given simple models, the overall population may be poorly fit by a given simple model – poor fit may be evidence for existence of subpopulations. For example, given two equal subpopulations, both normally distributed, if they have the same standard deviation and different means, the overall distribution will exhibit low kurtosis relative to a single normal distribution – the means of the subpopulations fall on the shoulders of the overall distribution. If sufficiently separated, these form a bimodal distribution, otherwise it simply has a wide peak. Further, it will exhibit overdispersion relative to a single normal distribution with the given variation. Alternatively, given two subpopulations with the same mean and different standard deviations, the overall population will exhibit high kurtosis, with a sharper peak and heavier tails (and correspondingly shallower shoulders) than a single distribution

See also


  1. ^ "Glossary of statistical terms: Population". Retrieved 22 February 2016.
  2. ^ Weisstein, Eric W. "Statistical population". MathWorld.
  3. ^ Yates, Daniel S.; Moore, David S; Starnes, Daren S. (2003). The Practice of Statistics (2nd ed.). New York: Freeman. ISBN 978-0-7167-4773-4. Archived from the original on 2005-02-09.
  4. ^ "Glossary of statistical terms: Sample". Retrieved 22 February 2016.

External links

This page was last edited on 25 September 2019, at 02:16
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