Review Sheet for Exam #1
G&W
Chapters 1 – 6
(excluding section 6.5)
[Please
note: This review sheet is intended to help you prepare for the exam. While it
is designed to be fairly comprehensive in scope, it is not necessarily an
exhaustive list of all possible exam material. All material from the texts,
lectures, and labs (from 1/28 through and including 2/13) is fair game for the
exam.]
Conceptual
Questions
|
What
is the difference between descriptive and inferential statistics? |
|
What
are operational definitions and why are they used? |
|
Why
is it important to identify the scale of measurement for a variable? |
|
Distinguish
between percentiles and percentile ranks. |
|
What
are the three primary characteristics of a frequency distribution graph? |
|
Why
do we measure central tendency? |
|
In
what ways can central tendency be measured? When is each measure of central
tendency most appropriate? |
|
How
are measures of central tendency affected by the shape of a distribution and
changes in the distribution (e.g., the presence of extreme scores, addition
of scores, etc.) |
|
What
is meant by variability? |
|
Define
standard deviation and distinguish between standard deviation and variance. |
|
Why
do we divide SS by N when computing population variance, but divide SS by n
– 1 when computing variance for a sample? Explain what it means for sample
variance to provide an unbiased estimate of the population variance. |
|
What
are degrees of freedom? |
|
How
do changes in the data set (addition of scores, adding a constant to each
score, etc.) impact the standard deviation? |
|
How
is variability influenced by extreme scores, sample size, sampling
stability, and open-ended distributions? |
|
What
role does variability play in descriptive and inferential statistics? |
|
Why
do we use z-scores? |
|
What
are the properties of any distribution of z-scores? |
|
What
are the properties of a normal distribution? |
|
What
is the difference between the normal distribution and the standard normal
distribution? |
|
What
are the dangers of choosing a sample that is not random? |
|
Distinguish
between independent, dependent, and mutually exclusive outcomes. |
|
Determine
whether a variable is discrete or continuous. |
|
Determine
whether a variable is measured on a nominal, ordinal, interval, or ratio
scale. |
|
Determine
whether something is an example of a descriptive or inferential statistic. |
|
Determine
whether research is correlational, experimental, or quasi-experimental. |
|
Identify
independent, dependent, and confounding variables. |
|
Make
all the various types of frequency distribution tables and be able to
interpret information from the tables. |
|
Make
all various types of frequency distribution graphs (and know when to use
each type). |
|
Describe
a frequency distribution graph with respect to its three defining
characteristics. |
|
Create
a stem and leaf display. |
|
Find
a percentile rank or percentile (with and without interpolation). |
|
Compute
the mean, median, and mode for a given data set. |
|
Compute
the 5-number summary for a given data set. |
|
Compute
the range, interquartile range, semi-interquartile range, variance, and
standard deviation for a data set. |
|
Understand
how measures of central tendency & variability are influenced by changes
in the data and know when to use each type of measure. |
|
Determine
the shape of a distribution from central tendency & variance
information. |
|
Transform
X values into z-scores and transform z-scores into X values. |
|
Use
z-scores to make comparisons and to create standardized distributions with a
given mean and standard deviation. Determine the probability of an event (or
combination of events). |
|
Use
the unit normal table to determine the probabilities for events that are
normally distributed. |
|
Use
the unit normal table to find the specific score associated with given
probabilities or proportions. |
|
Find
percentiles and percentile ranks for scores in a normal distribution. |
|
Interpret
SPSS output for descriptive statistics and frequencies. |
|
empirical
|
|
population
and sample
|
|
parameter
and statistic
|
|
descriptive
& inferential statistics
|
|
sampling
error
|
|
random
selection (sampling)
|
|
variable
|
|
correlation
study
|
|
experiment
|
|
independent
& dependent variable
|
|
experimental
& control groups
|
|
between
groups v. within groups designs
|
|
confounding
variable
|
|
random
assignment
|
|
quasi-experiment
|
|
differential
& time-series research
|
|
hypothesis
|
|
construct
|
|
operational
definition
|
|
scales
of measurement
|
|
discrete
vs. continuous variables
|
|
real
limits
|
|
frequency
distribution table
|
|
simple
frequency distribution (grouped & ungrouped)
|
|
relative
frequency distribution (proportions & percents)
|
|
cumulative
frequency distribution (simple frequencies)
|
|
cumulative
relative frequency distribution (cumulative %’s)
|
|
percentile
ranks and percentiles
|
|
interpolation
|
|
apparent
vs. real limits
|
|
frequency
distribution graphs: histogram, bar graph, polygon
|
|
relative
frequency curves
|
|
symmetrical
vs. skewed distributions
|
|
kurtosis
|
|
stem
and leaf display
|
|
central
tendency
|
|
mean
& weighted mean
|
|
median
|
|
mode
(unimodal, bimodal)
|
|
variability
|
|
range
|
|
interquartile
& semi-interquartile range
|
|
5-number
summary
|
|
standard
deviation
|
|
variance
|
|
deviation
score
|
|
sum
of squares (SS)
|
|
degrees
of freedom
|
|
biased
& unbiased statistics
|
|
standard
score
|
|
standardized
distribution
|
|
z-score
and z-distribution
|
|
probability
|
|
mutually
exclusive outcomes
|
|
independent
and dependent outcomes
|
|
addition
and multiplication rules
|
|
sampling
with replacement
|
|
normal
distribution
|
|
standard
normal distribution
|
|
unit
normal table
|