PSY 555 HW#1

1.1. The entire student body of your college or university would be considered a population under any circumstances in which you want to generalize only to the student body of your college or university and no further.

1.2. When you want to generalize or make inferences about a large population of students (all U.S. students, for example), then the student body of your college or university would be considered a sample.

1.4. Not all residents are listed in the phone book and thus not all residents have an equal chance of being included in the sample.  Transients, low income people, people with unlisted numbers (including famous people), and especially women and children would be underrepresented.  Business or professional people with more than one phone would be overrepresented.

1.16.The implication is that speed is a poor measure of learning unless we assume that the animal that suddenly went to sleep had forgotten all he ever knew about the task (b/c it is unlikely that zero speed [the animal sleeping] truly indicates that the rat has not learned anything, particularly after twelve trials).

1.    A discrete variable is one where information is generally categorized, such that all observations may only have a few values.  A continuous variable is one that can assume any value between the endpoints of a scale.  An example of discrete scale is gender, which will be coded with only two possible values (one for each gender).  An example of a continuous variable is length (which can assume an infinite number of values, depending on how precise you choose your measurement to be).  The distinction between discrete and continuous variables is tricky though, in that you can downgrade the way you code a continuous variable such that any continuous variable could be/resemble a discrete variable.  However, you cannot make a discrete variable continuous.

2.  Nominal Scale: race, gender

Ordinal Scale: rank in a race or marathon

Interval Scale: calendar year, temperature (Fahrenheit/Celsius scales)

Ratio Scale:  temperature (Kelvin scale), weight

3.   Descriptive statistics summarize the data and distribution, so you would use them to describe the data in a concise manner.  Inferential statistics test that data (in a variety of ways), so you would use these statistics when you wanted to draw a conclusion about some population based on your sample.  Examples of descriptive statistics include the measures of central tendency (mean, mode, median, etc.), measures of variability (variance, standard deviation, etc.), skewness values, etc.  Examples of inferential statistics include t-tests, Pearson's r, ANOVA, Chi-squared, etc.