Chapter 8 : Sampling Methods and the Central Limit Theorem

Chapter 8 : Sampling Methods and the Central Limit Theorem

1. WHY ?

   • time consuming
   • cost may be prohibitive
   • physical impossibility
   • The destructive nature of some tests.
   • The sample results are adequate.

2. A probability sample is a sample selected such that each item or person in the population being studied has a known likelihood of being included in the sample.

3. 4 Sampling method

   • Simple Random Sample
   • Systematic Random Sampling
   • Stratified Random Sampling
   • Cluster Sampling

4. Simple Random Sample : A sample selected so that each item or person in the population has the same chance of being included.

5. Systematic Random Sampling : The items or individuals of the population are arranged in some order. A random starting point is selected and then every kth member of the population is selected for the sample.

6. Stratified Random Sampling : A population is first divided into subgroups, called strata, and a sample is selected from each stratum. Useful when a population can be clearly divided in groups based on some characteristics

7. Cluster Sampling: A population is divided into clusters using naturally occurring geographic or other boundaries. Then, clusters are randomly selected and a sample is collected by randomly selecting from each cluster.

8. Sampling Distribution of the Sample Mean

   • a probability distribution consisting of all possible sample means of a given sample size selected from a population
   • = Sum of all sample means / Total number of samples

9. Central Limit Theorem

   • all samples of a particular size are selected from any population, the sampling distribution of the sample mean is approximately a normal distribution.

10. SIGMA

    • KNOWN
    • UNKNOWN

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