OUTLINE FOR CHAPTER
Foundations of Inferential Statistics
- A brief review of what's done in descriptive statistics
- An overview of the basic goal of inferential statistics
- Statistical Inference
- The basic notions of (1) a sample, (2) a population, and (3) an
- Two kinds of populations: "tangible" and "abstract"
- Picturing the sample, the population, and the direction of the
- The Concepts of "Statistic" and "Parameter"
- 4 basic questions a researcher must answer before making
an inferential guess:
- What's the population?
- How will samples be taken?
- What characteristics will be measured?
- What's the "statistical focus"?
- Two important concepts: "statistic" and "parameter"
- Representing a single concept (such as the mean) with different
symbols to signify that concept (1) in the population and (2) in
- Types of Samples
- Probability samples:
- Simple random samples
- Stratified random samples
- Systematic samples
- Cluster samples
- Nonprobability samples:
- Purposive samples
- Convenience samples
- Quota samples
- Snowball samples
- Three Sample-Related Problems That Sabbotage Statistical
- A low "response rate":
- Why it's a problem?
- Preventing low responses rates & dealing with them once
- Refusals to participate:
- Why they create a problem
- Comparing participants with nonparticipants
- Causes of this problem (sometimes called "drop-out" or "mortality"
- Checking to see if attrition restricts generalization
- A Few Warnings
- There may be a mismatch between the data-suppliers & the intended
- It's the quality of the sample (not its size)
that makes inferential statistics work.
- The term "random" is sometimes used when it really should not
- Statistical inferences are worthless if there's an inadequate
description of . . .
- the population, if it's a tangible population
- the sample, if it's connected to an abstract population