PSY 555 HW#1

ANSWERS

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

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.