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 |
|
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 |
|
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 |
