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## Basic Concepts

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. Two ways of getting around this ...

### Random Sampling

 Choose a subset of the population ensuring that each member of the population has an equivelant chance of being sampled Examine that sample and use your observations to draw inferences about the population Example: Voting Polls, Television Ratings 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

### 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 If random assignment is nor used then the internal validity of the experimental results may be compromised
 Example: Text book manipulation across years

### Variables

 Assume we have a random sample of subjects that we have randomly assigned to treatment groups
 Example: Stop-smoking study
 Now we must select the variables we wish to study, with the term variable referring to a property of an object or event that can take on different values
 Examples: # of cigs smoked, abstinance after one week
 Note the distinction; # of cigarettes smoked is a continuous variable, whereas abstinance is a categorical variable 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

### What Do We Do With The Data?

 Descriptive Statistics are used to describe the data set
 Examples: graphing, calculating averages, looking for extreme scores
 Inferential Statistics allow you to infer something about the the parameters of the population based on the statistics of the sample, and various tests we perform on the sample
 Examples: Chi-Square, T-Tests, Correlations, ANOVA NOTE: See section in book on measurement scales