This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.

3. Created by Tom Wegleitner, Centreville, Virginia Section 1-1 Overview

4. Overview A common goal of surveys and other data collecting tools is to collect data from a smaller part of a larger group so we can learn something about the larger group. In this section we will look at some of ways to describe data.

10. Created by Tom Wegleitner, Centreville, Virginia Section 1-2 Types of Data

15. Working with Quantitative Data Quantitative data can further be distinguished between discrete and continuous types.

18. Levels of Measurement Another way to classify data is to use use levels of measurement. Four of these levels are discussed in the following slides.

25. Created by Tom Wegleitner, Centreville, Virginia Section 1-3 Critical Thinking

30. Figure 1-1

31. To correctly interpret a graph, we should analyze the numerical information given in the graph instead of being mislead by its general shape.

33. Figure 1-2 Double the length, width, and height of a cube, and the volume increases by a factor of eight

35. 97% yes: “Should the President have the line item veto to eliminate waste?” 57% yes: “Should the President have the line item veto, or not?”

38. Created by Tom Wegleitner, Centreville, Virginia Section 1-4 Design of Experiments

47. Random Sampling selection so that each has an equal chance of being selected

48. Systematic Sampling Select some starting point and then select every K th element in the population

49. Convenience Sampling use results that are easy to get

50. Stratified Sampling subdivide the population into at least two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum)

51. Cluster Sampling divide the population into sections (or clusters); randomly select some of those clusters; choose all members from selected clusters