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BASIC CONCEPTS

BASIC CONCEPTS. Outline. Population Random Sampling Random Assignment Variables What do we do with the data?. Population. The entire collection of events that you are interested in. Although we wish to make claims about the entire population, it is often too large to deal with.

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BASIC CONCEPTS

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  1. BASIC CONCEPTS Chapter 1

  2. Outline • Population • Random Sampling • Random Assignment • Variables • What do we do with the data? Chapter 1

  3. Population • The entire collection of events that you are interested in. • Although we wish to make claims about the entire population, it is often too large to deal with. • There are two ways of getting around this. . . . Chapter 1

  4. Random Sampling • Choose a subset of the population ensuring that each member of the population has an equivalent chance of being sampled. • Examine that sample and use your observations to draw inferences about the population. • Example : Voting polls, television ratings Chapter 1

  5. Random Sampling • Note, however, that the inferences drawn are only as good as the randomness of the sample. • If the sample is not random, it may not be representative of the population. When a sample is not representative of its parent population, the external validity of any inferences is called into question. • Example : Most psychology experiments Chapter 1

  6. Random Assignment • When studying the effects of some treatment variable, it is also important to randomly assign subjects to treatments. • Random assignment reduces the likelihood that groups differ in some critical way other than the treatment. Chapter 1

  7. Random Assignment • If random assignment is not used then the internal validity of the experimental results may be compromised Chapter 1

  8. Variables • Assume we have a random sample of subjects that we have randomly assigned to treatment groups. • Example: stop-smoking study Chapter 1

  9. Variables • Now we must select the variables we wish to study, with the term variable referring to a property of an object or even that can take on different values. • Example: # of cigs smoked, abstinence after one week. • Note the distinction; # of cigarettes smoked is a continuous variable, whereas abstinence is a categorical variable. Chapter 1

  10. Variables • Another distinction related to variables concerns variables we measure (dependent variables) versus variables we manipulate (independent variables). • For Example: Whether or not we give a subject the stop-smoking treatment would be the independent variable, and the # of cigarettes smoked would be a dependent variable. Chapter 1

  11. What do we do with the data? • Descriptive Statistics are used to describe the data set. • Examples: graphing, calculating, averages, looking for extreme scores. Chapter 1

  12. What do we do with the data? • Inferential Statistics allow you to infer something about the parameters of the population based on the statistics of the sample, and the various tests we perform on the sample. • Examples: Chi-Square, T-Tests, Correlations, ANOVA • NOTE: See sections in book on measurement scales. Chapter 1

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