Fall 2015
Final Exam Study Guide
null hypothesis
alternative hypothesis (also known as the research/test
hypothesis)
argument by contradiction
hypothesis-testing process (5 steps)
retaining vs. rejecting the null hypothesis
alpha levels, level of significance
one-tailed vs. two-tailed tests- What is the difference? In what
situations do we use each? Which of the two (one-tailed or
two-tailed) make it easier to reject the null hypothesis?
alpha error, type I error- What is it?
beta error, type II error- What is it?
power- What is it? How do we determine it? (1 – β)
Know the relationships between alpha, beta and power
Central Limit Theorem- What is it? Why is it important? How does
sample size influence the standard error of the mean?
sampling distribution of the mean
standard error of the mean (also known as the standard deviation
of sample means)
What is the difference between a population distribution and a
sampling distribution?
Know how to calculate the standard error of the mean
critical z values for one tailed test, two tailed test (at the
.05 and .01 alpha levels)
Z test- assumptions; know when to use and how to compute; know
how to state the null and alternative hypotheses.
One sample t-test assumptions; know when to use and how to
compute; know how to state the null and alternative
hypotheses.
Know the critical z values for a one-tailed and two-tailed test
at alpha .05 and .01.
sampling distribution of the difference between means
standard error of the difference between means (also known as the
standard deviation of the difference)
Sampling distribution of the mean difference
Standard error of the mean difference (also known as the standard
deviation of the mean difference)
Be able to identify independent and dependent variables.
Degrees of freedom (df) and the t-tests; know how to determine
them for each type of t-test.
Z table and t-table- what is the difference between them? When do
we use one or the other? When are they the same (no
difference between the two tables)?
Two sample independent t-test- assumptions; know when to use and
how to compute; know how to state the null and alternative
hypotheses.
Dependent t-test (Two sample dependent, paired t-test) assumptions; know when to use and how to compute; know how to
state the null and alternative hypotheses.
What are the Scheffe and Tukey statistical test?
Analysis of variance (ANOVA)-assumptions
F-statistic (F value); Know how to determine the degrees of
freedom and critical f values. Know what it tells us.
Between group variance (SSB), mean squares between (MSB). What
does it measure?
Within group variance (SSW), mean squares within (MSW). What does
it measure?
Under what circumstance does the relationship of t²= F hold up?
Principle of variance constancy
F-distribution
Post-hoc tests
Chi-square- two types: (1) goodness of fit (aka one-way chisquare) (2) test of independence (aka two-way chi-square)
Chi-square- assumptions; how to compute
Phi Correlation
Cross-tabulation or Contingency table- What is it?
Pearson correlation coefficient- What is it? When is it used?
(under what conditions is it used; assumptions)
Scatter plot or Scatter gram- what is it? What does it tell us?
Direct or Positive relationships
Inverse or Negative relationships
Perfect direct relationship
Perfect inverse relationship
Limitations of Pearson correlation coefficient (4 limitations)
Line of best fit or Least squares line
Equation for a straight line
Simple regression analysis; Multiple linear regression
Coefficient of determination (r²)
Coefficient of Non-determination (k²)
Standard error of the estimate
Predictor variable, independent variable (X)
Predicted variable, dependent variable (Y)
Ŷ (Y hat)
Sum of squared errors
SPSS extra credit questions
Review the handout available on my homepage. You should know
about variable names, variable labels, value labels, split
file, select cases, crosstabs, etc for the final exam.