Download Study_Guide_for_Exam..

Study Guide for Exam 1
I’ve put together a brief study guide for the exam. All this stuff may appear in some form on the
exam, but the content from lectures 3, 5, and 6 (variability, the normal distribution, and sampling
distributions) are especially important and will be well-represented on the exam.
Lecture 1:




Basic definitions
o Know the definitions for: variable, sample, population, parameter, statistic,
inferential & descriptive statistics, and sampling error.
o Understand the relationships between samples & populations and statistics &
parameters
Measurement Scales & Data Types
o Know the 4 different measurement scales
o Know the difference between continuous and discrete variables
o Know the difference between qualitative/categorical and
quantitative/measurement data
Basic research designs
o Know the differences between basic research designs
 Experimental
 Correlational
 Quasi-experimental
o Know the functional difference between dependent and independent variables
o Know the definitions for random sampling and random assignment and the
consequences of failing to sample or assign randomly
Understand statistical notation and the order of operations (you’ll need this for the
computational part anyway)
Lecture 2:

Displaying Data
o Understand how to build and read regular and grouped frequency and relative
frequency distribution tables
o Understand the relationship between frequencies, relative frequencies, and
percentages and know how to compute all three quantities from a set of raw
scores
o Know how to read and construct histograms (I won’t ask you to construct any on
the exam, but some questions may require that you understand how they are
constructed)
o Understand the difference between histograms and probability density curves
o Be able to recognize and know the properties of different distribution shapes.
 Symmetric
 Bimodal
 Unimodal
 Positively skewed

 Negatively skewed
Central Tendency
o Understand all three measures of central tendency and know how to compute
them
 Mean
 Median
 Mode
o Be able to identify the strengths and weaknesses of each measure.
o Understand how the shape of the distribution affects the different measures of
central tendency
o Understand how measures of central tendency are affected by extreme scores, by
adding scores to a sample, or by adding constants to all scores.
o Know how to compute weighted means to average statistics across multiple
samples or to compute means from frequency data.
Lecture 3:

Dispersion or Variability
o Know the definition of dispersion and why we need a measure of variability as
well as one of central tendency
o Know how to compute:
 Range
 Variance (for populations and samples)
 Standard deviation (for populations and samples)
 Average deviation
 Interquartile range
 Know how to compute percentile ranks and percentiles
o Understand the concept of statistics as biased or unbiased estimators and (at least
superficially) the reason for using n-1 instead of n as the denominator in the
equation for the sample variance.
o As with the measures of central tendency, you should understand how the
different measures of variability are affected by extreme scores, by adding scores
to a sample or by linearly transforming (i.e., adding and/or multiplying by a
constant) each of the scores in a sample
Lecture 4:


Interpretations of probability
o Know the definitions for the three interpretations
 Frequentist (or frequentistic)
 Classical (or analytic)
 Subjectivist (or Bayesian)
Basic Definitions:
o Know the definitions of a trial and an event
o Know the definitions of independent and dependent events
o Understand the difference between mutually exclusive events and independent
events
o Understand the difference between joint and conditional probabilities


Know how (and when) to use the multiplicative and additive laws to compute compound
probabilities from simple (marginal) probabilities
Understand the relationship between joint, conditional, and marginal probabilities
o Know how to compute a joint probability from conditional and marginal
probabilities
o Know how to compute a conditional probability from joint and marginal
probabilities
o Know how to compute a marginal probability from an exhaustive set of
conditional probabilities
Lecture 5:



Distributions in General
o Understand the relationship between distributions and probabilities
o Understand the difference between discrete and continuous variables
The Normal Distribution
o Understand the role of the normal distribution in statistics and the various reasons
it is used.
o Understand the relationship between the standard normal distribution and any
other normal distribution
 Know the parameters of the standard normal distribution
o Be sure to remember that the distribution is symmetrical and the relationship
between its three measures of central tendency
o Try to remember roughly how much of the distribution falls within the central
one (~68%) and two (~95%) standard deviations
z-Scores
o Know (and understand!) the formulas for computing z-scores for x-scores drawn
for arbitrary distributions and how to compute x-scores from z-scores given the
parameters of a normal distribution
o Remember to interpret z as “units of standard deviation from the mean”
o Understand how to compute z given a percentage or a proportion
 Remember that the table used on the test always gives tail probabilities
(i.e., always values smaller than 0.5) so if you want to determine the area
of the body of the curve, you have to look up the tail probability and
subtract it from one.
 Remember that computing z from a probability or percentage is the first
step in determining the percentile of a distribution with arbitrary mean
and standard deviation
o Know how to compute
 The probability area in the lower or upper tail of the distribution
 The probability area of the normal distribution within a given interval
 An x or z probable interval for any percentage given a mean and standard
deviation
Lecture 6:

Sampling distributions

o Make sure you understand the concept of sampling error and how sampling errors
give rise to a sampling distribution
 The sampling error of a statistic is the variability of a statistic from sample
to sample due to chance
 The sampling distribution of a statistic is a description of its sampling
error
o Know the relationship between the sampling distribution of a statistic and its
standard error. You should also clearly understand the difference between the
sampling distribution of a statistic and the population distribution of its
underlying scores
o Understand the difference between biased and unbiased estimators
o Understand the difference between sampling with and without replacement
Central Limit Theorem
o Make sure you understand the implications of the central limit theorem regarding
the sampling distribution of the mean:
 The mean of the sampling distribution of the mean is the population mean
of the underlying scores
 As the number of scores n used to compute the sample mean increases, the
standard error of the mean decreases
 Whatever the distribution of the parent population, the sampling
distribution of the mean approaches a normal distribution as the sample
size (n) increases.
o Know the equation for computing the standard error of the mean (  M ) from the
population variance (  ) and sample size (n)
o Know how to compute the z-transformation (z-statistic) for a sample mean given
the population mean (  ), the population standard deviation (  ), and the sample
size n.
Suggested Textbook Problems
3.6, 3.12, 3.20, 3.22, 4.12 (a,b,d,e), 4.16, 4.18, 4.26, 5.14, 5.18, 5.32, 6.14, 6.18, 6.20, 6.22, 7.22,
7.24