This chapter introduces elementary probabulity concepts and shows how they are related to inferential statistics.   A number of simple examples are provided and the relation to the Central Limit Theorem is considered, which is the logic needed for making inferences from samples to populations.
 
Introduction
Population Parameters and Sample Estimates
Random Sampling, Representativeness, and Bia
Developing the Logic of Inference: Expected Values and Extreme Values
Expected Values, Extreme Values, Theoretical Distributions, and the Law of Large Numbers
The Real World: Changing the Number of Samples
Increasing the Sample Size
Expected Values and the Changing Probability of Extreme Values with Increasing Sample Size
The Sampling Distribution of the Means and the Central Limit Theorem
Using Probability to Test a Hypothesis: Expected Values and Extreme Values
Hypothesis Testing: An Informal Introduction
Decision Rules
Interval Estimates and Extreme Values and Decision Rules
Reviewing the α-level, Type I Errors, and Introducing Type II Errors
Summary