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Biostatistics
Unit 5 – Samples
Sampling distributions
Sampling distributions are important in the understanding of statistical inference. Probability distributions permit us to answer questions about sampling and they provide the foundation for statistical inference procedures.
Definition
The sampling distribution of a statistic is the distribution of all possible values of the statistic, computed from samples of the same size randomly drawn from the same population. When sampling a discrete, finite population, a sampling distribution can be constructed. Note that this construction is difficult with a large population and impossible with an infinite population.
Construction of sampling distributions
1. From a population of size N, randomly draw all possible samples of size n. 2. Compute the statistic of interest for each sample.3. Create a frequency distribution of the statistic.
Properties of sampling distributions
We are interested in the mean, standard deviation and appearance of the graph (functional form) of a sampling distribution.
Types of sampling distributions
We will study the following types of sampling distributions.A) Distribution of the sample mean B) Distribution of the difference between two means
C) Distribution of the sample proportion D) Distribution of the difference between two proportions
Sampling distribution of
Given a finite population with mean () and variance (). When sampling from a normally distributed population, it can be shown that the distribution of the sample mean will have the following properties.
Properties of the sampling distribution
1. The distribution of will be normal2. The mean , of the distribution of the values of
, will be the same as the mean of the population from which the samples were drawn; = .3. The variance, , of the distribution of
, will be equal to the variance of the population divided by the sample size;
= .
Standard error
The square root of the variance of the sampling distribution is called the standard error of the mean or the standard error.
Nonnormally distributed populations
When the sampling is done from a nonnormally distributed population, the central limit theorem is used.
The central limit theorem
Given a population of any nonnormal functional form with mean () and variance (2) , the sampling distribution of , computed from samples of size n from this population will have mean, , and variance, 2/n, and will be approximately normally distributed when the sample is large (30 or higher).
The central limit theorem
Note that the standard deviation of the sampling distribution is used in calculations of z scores and is equal to
Example
Given the information below, what is the probability that x is greater than 53?
(1) Write the given information
= 50 = 16 n = 64 x = 53
Example(2) Sketch a normal curve
Example
(3) Convert x to a z score
Example
(4) Find the appropriate value(s) in the table
A value of z = 1.5 gives an area of .9332. This is subtracted from 1 to give the probability P (z > 1.5) = .0668
Example
(5) Complete the answer
The probability that x is greater than 53 is .0668.
Distribution of the difference between two means
It often becomes important to compare two population means. Knowledge of the sampling distribution of the difference between two means is useful in studies of this type. It is generally assumed that the two populations are normally distributed.
Sampling distribution of
Plotting sample differences against frequency gives a normal distribution with mean equal to which is the difference between the two population means.
Variance
The variance of the distribution of the sample differences is equal to
Therefore, the standard error of the differences between two means would be equal to
Converting to a z score
To convert to the standard normal distribution, we use the formula
We find the z score by assuming that there is no difference between the population means.
Sampling from normal populations
This procedure is valid even when Sampling from normal populations the population variances are different or when the sample sizes are different. Given two normally distributed populations with means, and , and variances, and , respectively.
(continued)
Sampling from normal populations
The sampling distribution of the difference, , between the means of independent samples of size n1 and n2 drawn from these populations is
normally distributed with mean, , and
variance,
Example
In a study of annual family expenditures for general health care, two populations were surveyed with the following results:
Population 1: n1 = 40, = $346
Population 2: n2 = 35, = $300
Example
If the variances of the populations are
= 2800 and = 3250, what is the probability of obtaining sample results as large as those shown if there is no difference in the means of the two populations?
Solution
(1) Write the given information
n1 = 40, = $346, = 2800
n2 = 35, = $300, = 3250
Solution(2) Sketch a normal curve
Solution
(3) Find the z score
Solution
(4) Find the appropriate value(s) in the table
A value of z = 3.6 gives an area of .9998. This is subtracted from 1 to give the probability P (z > 3.6) = .0002
Solution
(5) Complete the answer
The probability that is as large as given is .0002.
Distribution of the sample proportion ( )
While statistics such as the sample mean are derived from measured variables, the sample proportion is derived from counts or frequency data.
Properties of the sample proportion
Construction of the sampling distribution of the sample proportion is done in a manner similar to that of the mean and the difference between two means. When the sample size is large, the distribution of the sample proportion is approximately normally distributed because of the central limit theorem.
Mean and variance
The mean of the distribution, , will be equal to the true population proportion, p, and the variance of the distribution, , will be equal to p(1-p)/n.
The z-score
The z-score for the sample proportion is
Example
In the mid seventies, according to a report by the National Center for Health Statistics, 19.4 percent of the adult U.S. male population was obese. What is the probability that in a simple random sample of size 150 from this population fewer than 15 percent will be obese?
Solution
(1) Write the given information
n = 150 p = .194
Find P( < .15)
Solution(2) Sketch a normal curve
Solution
(3) Find the z score
Solution
(4) Find the appropriate value(s) in the table
A value of z = -1.36 gives an area of .0869 which is the probability P (z < -1.36) = .0869
Solution
(5) Complete the answer
The probability that < .15 is .0869.
Distribution of the difference between two proportions
This is for situations with two population proportions. We assess the probability associated with a difference in proportions computed from samples drawn from each of these populations. The appropriate distribution is the distribution of the difference between two sample proportions.
Sampling distribution of
The sampling distribution of the difference between two sample proportions is constructed in a manner similar to the difference between two means.
(continued)
Sampling distribution of
Independent random samples of size n1 and n2 are drawn from two populations of dichotomous variables where the proportions of observations with the character of interest in the two populations are p1 and p2 , respectively.
Mean and variance
The distribution of the difference between two
sample proportions, , is approximately normal. The mean is
The variance is
These are true when n1 and n2 are large.
The z score
The z score for the difference between two proportions is given by the formula
Example
In a certain area of a large city it is hypothesized that 40 percent of the houses are in a dilapidated condition. A random sample of 75 houses from this section and 90 houses from another section yielded difference, , of .09. If there is no difference between the two areas in the proportion of dilapidated houses, what is the probability of observing a difference this large or larger?
Solution
(1) Write the given information
n1 = 75, p1 = .40
n2 = 90, p2 = .40
= .09
Find P( .09)
Solution(2) Sketch a normal curve
Solution
(3) Find the z score
Solution
(4) Find the appropriate value(s) in the table
A value of z = 1.17 gives an area of .8790 which is subtracted from 1 to give the probability
P (z > 1.17) = .121
Solution
(5) Complete the answer
The probability of observing
of .09 or greater is .121.
fin