Chapter 6 Introduction to Inferential Statistics l l

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Chapter 6, Introduction to Inferential Statistics l l l Sampling & the Sampling Distribution

Chapter 6, Introduction to Inferential Statistics l l l Sampling & the Sampling Distribution Techniques for Probability Sampling EPSEM Sampling Techniques The Sampling Distribution Symbols and Terminology

Purpose of Inferential Statistics l l l Learn about the characteristics of a population,

Purpose of Inferential Statistics l l l Learn about the characteristics of a population, based on samples. Estimation procedures - a “guess” is made, based on what is known about the sample. Hypothesis testing - validity of a hypothesis about the population is tested against sample outcomes.

Probability Sampling l l l A sample is likely to be representative if it

Probability Sampling l l l A sample is likely to be representative if it is selected by the EPSEM principle. Every element in the population must have an equal probability of selection for the sample. EPSEM - Equal Probability of Selection Method

Generating Simple Random Samples l l List of all elements or cases in the

Generating Simple Random Samples l l List of all elements or cases in the population. Develop a system for selection that guarantees that every case has an equal chance of being selected for the sample.

Systematic Sampling l l Only the first case is randomly selected. Thereafter every kth

Systematic Sampling l l Only the first case is randomly selected. Thereafter every kth case is selected.

Stratified Sampling l l Population is divided into sublists according to a relevant trait.

Stratified Sampling l l Population is divided into sublists according to a relevant trait. Sample is drawn from the sublist.

Cluster Sampling l l l Groups of cases are selected rather than single cases.

Cluster Sampling l l l Groups of cases are selected rather than single cases. Clusters are often based on geography and the selection of clusters proceeds in stages. A less accurate representation of the population.

Characterizing a Variable Requires three types of information: 1. The shape of its distribution.

Characterizing a Variable Requires three types of information: 1. The shape of its distribution. 2. Some measure of central tendency. 3. Some measure of dispersion.

Distributions in Inferential Statistics 1. 2. 3. Sample - allows the researcher to learn

Distributions in Inferential Statistics 1. 2. 3. Sample - allows the researcher to learn about the population. Population -making inferences to the population is the purpose of inferential statistics. Sampling - because of the laws of probability, a great deal is known about this distribution.