Summary of Statistical Tests

Note that not all tests listed in the table are covered in this course. The one's that are covered are linked to the appropriate material in the course.

This table can help you decide when to use which statistic. The first issue is the level of measurement of the data. The next issue is whether we are trying to assess causal relationships, or do we just want to measure the strength and direction of a relationship. If the experimental approach is taken then we need to look very carefully at the characteristics of the sample for all independent variables (IVs).

Level of
Meas-
urement
Sample Characteristics
Corr-
ela
tion
1-
Sample
2-Sample K-Sample
(i.e., >2)
Indep-
endent
Dependent Indep-
endent
Dependent
Categorical
or Nominal
x2
or
Binomial
x2
Macnamar's
x2
x2
Cochran's
Q

Rank or
Ordinal
Mann-
Whitney U
Wilcoxin
Matched
Pairs
Signed
Ranks
Kruskal
Wallis H
Friedman's
ANOVA
Spear-
man's
rho
Parametric
(Interval
& Ratio)
z test
or
t
test

t test
between
groups
t test
within
groups
1-way
ANOVA

between
groups
1-way
ANOVA

(within or
repeated
measure)
Pear-
son's
r
Factorial (2-way) ANOVA
can handle more than 1 IV