Study Guide for Statistics Test #1
The materials you need to know for the test appear below, and are divided into concepts (know
definitions, uses, examples, etc.) and computations (know how to do problems with real data). There
are no guarantees that this is a completely exhaustive list of possible question items. You are
responsible for the information presented in class, in the text, and from homework problems (by hand
and using SPSS). If you know that material well, and “check” the list below to make sure you have
these things covered, you will do well. Bring pencils and a basic calculator to the exam on Monday.
Chapter 1: Introduction to Statistics
I.
Concepts
A. Statistics and Science
1. Definition and uses (what they do, what questions they answer)
2. Descriptive vs Inferential
3. From Theory to Statistics:
(a) Constructs, variables, operational definitions
(b) Data, variables, constants
4. Populations versus Samples, Sampling, Sampling Error (margin of error)
5. Parameters versus Statistics
B. Research Design
1. Experimental, Quasi-Experimental, Correlational (aka Survey, Observational, Naturalistic,
Non-experimental design etc.)
2. Experimental Design:
(a) Experimental vs. Control group/level/condition
(b) Random assignment, manipulation
3. Independent versus Dependent variable (and confounding variable)
4. Representativeness, Generalizability
C. Data and variable types
1. Qualitative vs quantitative
2. Discrete vs. Continuous (and understand the relation to real limits – next point)
3. Real limits (when do you need to take into consideration?)
4. Scales of Measurement (Nominal, Ordinal, Interval, Ratio)
(a) Uses and examples of each type of scale
(b) Why is scale of measurement important?
D. Statistical Notation
1. Know things like what X and Y represent, what N, n, and f represent, etc.
2. Summation using  (see II.A., below)
3. Greek versus Roman letters – what’s the difference? when do you use each?
II. Computations
A. Manipulate data tables to come up with sums, such as: X², (X)², (X-Y)², X-Y²
Conley Stats Winter 2006
Chapter 2: Frequency Distributions
I.
Concepts
A. Frequency distribution tables
B. Frequency distribution graphs (histograms, polygons, bar graphs)
C. Smooth curves: Only used for graphing populations!
D. The use and misuse of graphs (height/length, origin, breaks) – also see p. 90, 95
E. Shapes of distributions
1. Symmetrical, Skewed (positive vs. negative; tail)
2. Unimodal, bimodal, rectangular, etc.
F. Proportions (relative frequencies), percentiles, percentile rank
1. Cumulative frequency, cumulative percentage
2. Interpolation
II. Computations
A. Frequency distribution tables
1. grouped frequency distribution (including how to determine class intervals)
B. Frequency distribution graphs (histograms, polygons, bar graphs)
C. Be able to calculate N, X, M from frequency distribution table or graph
D. Stem and leaf displays
Chapter 3: Central Tendency
I.
Concepts
A. Measures of Central Tendency: Mean, Median, Mode
1. Definitions, uses, characteristics, concepts, visual aids
2. Selecting a measure of central tendency: When you can and should use each; Pros and
cons of their use
3. Effect of distribution (e.g., skew), and adding/changing scores, on measures of central
tendency
II. Computations
A. Mean
1. population mean, sample mean
2. weighted mean
B. Median
1. odd number or even number of scores
2. tied medians
3. Several scores of the same value clustered at the median. You do need to consider real
limits here, but if you approach a computation problem using the visual methods we used
in class (e.g., divide up blocks on a frequency histogram, or line up the scores in a row), or
thinking of it like interpolation, you’ll get the same answer as with the book’s formula on
page 85.
C. Mode
Conley Stats Winter 2006
Chapter 4: Variability
I.
Concepts
A. Variability in general
B. Measures of variability: Range, (Semi-)Interquartile Range, Variance, Standard Deviation
1. Definitions, uses, characteristics, concepts, visual aids
2. Selecting a measure of central tendency: When you can and should use each; Pros and
cons of their use
3. Effect of distribution (e.g., skew), and adding/changing scores, on measures of central
tendency
C. In addition, for Variance and Standard Deviation know about:
1. Deviation scores, Sum of squares
2. Sampling (population to sample), estimation, bias, degrees of freedom (p. 116-122)
3. Impossible to have negative values for SS, variance, and SD
4. How to eyeball a set of data (e.g., on a histogram) and estimate the SD (this will help you
notice if your calculations are incorrect). See page 114.
5. Transformations of scale (p. 124)
6. Variance and inferential statistics (p. 126-128)
II. Computations
A. Range
B. (Semi-)Interquartile Range
C. Deviation scores
D. Sum of squares
E. Variance (population or sample)
F. Standard Deviation (population or sample)
Relationships Between/Among Concepts

Relationship between scales of measurement and:
o
graphs
o
central tendency and variability

Relationship between central tendency and variability

Relationship between central tendency and variability (combined) and raw data
o
E.g., if you know the Mean and SD, you should be able to provide a rough sketch of
the data (with no other info, assume it’s symmetrical). See p. 125.
o
Given a picture of the data (e.g., histogram), you should have a sense of what the
mean, median, and mode are, and what the variability (range, IQR, SD) is.
Conley Stats Winter 2006