1. Chapter Six Normal Curves and Sampling Probability Distributions

2. Chapter 6 Section 4 Sampling Distributions

3. Review of Statistical Terms

4. Population

5. Sample A subset of measurements from a population.
We use information obtained from a sample to
make inferences about the population.
For a given population, there is a very large
number of possible samples.

6. A numerical descriptive measure of a population.
Parameter
A numerical descriptive
measure of a population.

7. Examples of Population Parameters
The population mean, denoted by μ, is a population parameter.
The population standard deviation, denoted by σ, is a population parameter.
The population value in a binomial experiment of the probability of success for one trial, denoted by p, is a population parameter.

9. Statistic

10. Examples of Sample Statistics

11. Sample statistics are used to make inferences about population parameters
Statistic are used to estimate the value of a parameter.
Statistics are used to make decisions about the value of a parameter.
If we were somehow able to produce all the possible samples of the same size, calculate each sample mean,
and then observe the resulting distribution, we would
be examining what is called the sampling distribution.
When we are interested in investigating a population
mean, we must know about the sampling distribution
for sample means of a given sample size.

12. Principal types of inferences
Estimation: In this type of inference, we estimate the value of a population parameter.
Testing: In this type of inference, we formulate a decision about the value of a population parameter.
Regression: In this type of inference, we make predictions or forecasts about the value of a statistical variable.

13. Sampling distribution
A sampling distribution is a probability distribution of a sample statistic based on all possible simple random samples of the same size from the same population.

14. END OF SECTION 4