OUTLINE FOR CHAPTER 5 IN THE 6th EDITION Foundations of Inferential Statistics Introduction 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 inference Two kinds of populations: "tangible" and "abstract" Picturing the sample, the population, and the direction of the inference 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 the sample 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 Inferences A low "response rate": Why it's a problem? Preventing low responses rates & dealing with them once they occur Refusals to participate: Why they create a problem Comparing participants with nonparticipants Attrition: 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 population. 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 be used. 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