Chapter 1

• 1.1 Data from samples are used in inferential statistics to make an inference about the larger population.
• 1.2
  • a. The average grade for your statistics class would be a descriptive statistic because it’s being used only to describe the tendency of people in your class with respect to a statistics grade.
  • b. In this case, the average grade would be an inferential statistic because it is being used to estimate the results of a population of students taking statistics.
• 1.3
  • a. 1,500 Americans
  • b. all Americans
  • c. The 1,500 Americans in the sample know 600 people, on average.
  • d. The entire population of Americans has many acquaintances, on average. The sample mean, 600, is an estimate of the unknown population mean.
• 1.4 Discrete observations can take on only specific values, usually whole numbers; continuous observations can take on a full range of values.
• 1.5
  • a. These data are continuous because they can take on a full range of values.
  • b. The variable is a ratio observation because there is a true zero point.
  • c. On an ordinal scale, Lorna’s score would be 2 (or 2nd).
• 1.6
  • a. The levels of gender, male and female, have no numerical meaning even if they are arbitrarily labeled 1 and 2.
  • b. The three levels of hair length (short, mid-length, and very long) are arranged in order, but we do not know the magnitude of the differences in length.
  • c. The distances between probability scores are assumed to be equal.
• 1.7 Independent; dependent
• 1.8
  • a. There are two independent variables: beverage and subject to be remembered. The dependent variable is memory.
  • b. Beverage has two levels: caffeine and no caffeine. The subject to be remembered has three levels: numbers, word lists, and aspects of a story.
• 1.9
  • a. Whether or not trays were available
  • b. Trays were available; trays were not available.
  • c. Food waste; food waste could have been measured via volume or weight as it was thrown away.
  • d. The measure of food waste would be consistent over time.
  • e. The measure of food waste was actually measuring how much food was wasted.
• 1.10 Experimental research involves random assignment to conditions; correlational research examines associations where random assignment is not possible and variables are not manipulated.
• 1.11 Random assignment helps to distribute confounding variables evenly across all conditions so that the levels of the independent variable are what truly vary across groups or conditions.
• 1.12 Rank in high school class and high school grade point average (GPA) are good examples.
• 1.13
  • a. Researchers could randomly assign a certain number of women to be told about a gender difference on the test and randomly assign a certain number of other women to be told that no gender difference existed on this test.
  • b. If researchers did not use random assignment, any gender differences might be due to confounding variables. The women in the two groups might be different in some way (e.g., in math ability or belief in stereotypes) to begin with.
  • c. There are many possible confounds. Women who already believed the stereotype might do so because they had always performed poorly in mathematics, whereas those who did not believe the stereotype might be those who always did particularly well in math. Women who believed the stereotype might be those who were discouraged from studying math because “girls can’t do math,” whereas those who did not believe the stereotype might be those who were encouraged to study math because “girls are just as good as boys in math.”
  • d. Math performance is operationalized as scores on a math test.
  • e. Researchers could have two math tests that are similar in difficulty. All women would take the first test after being told that women tend not to do as well as men on this test. After taking that test, they would be given the second test after being told that women tend to do as well as men on this test.