Download Choosing the right statistic

Statistical Reporting Format
Statistic Letter (df) = obtained value, p decision
Examples:
A paired-samples t-test with 10 participants:
t(9) = 3.54, p<.05
A one-way between-subjects ANOVA:
F(2, 15) = 68.20, p<.05
If your results are NONSIGNIFICANT, you typically write p=NS.
Example: F(2, 15) = 1.32, p=NS
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Which statistical test
should I use?
yes
Are you counting up a
number of events which
have only one of two
possible outcomes?
yes
Independent
samples
T-test
Are the groups
composed of the
sample people?
yes
no
yes
BetweenSubjects TwoWay ANOVA
MixedFactorial
ANOVA
Are you comparing
two samples?
yes
no
no
yes
Z-test
no
no
Are you
comparing a
sample to a
population?
Sign
test
Pairedsamples
T-test
Do you know the
population standard
deviation?
Are you comparing
more than two
samples?
no
yes
Does each person
experience only one
condition?
no
Between-Subjects
One-Way ANOVA
One-Sample
t-test
no
WithinSubjects
One-Way
ANOVA
yes
Are the groups composed of
the sample people?
Do you have more
than one independent
variable?
no
yes
Are all the groups
composed of the
sample people?
yes
Within-Subjects
Two-Way ANOVA
Test
Statistic
df
Use this test when:
Sign Test
N/A
N/A
Evaluating a series of repeated events, such that each
event has one of two possible outcomes.
Z-test
Z
N/A
Comparing sample to a known population mean, when we
know the population standard deviation
One-sample t-test
t
N-1
Comparing sample to a known population mean, when we
don’t know the population standard deviation
Paired-Samples t-test
t
N-1
Comparing two samples, the data from which comes from
the same participants
Independent-Samples t-test
t
N-2
Comparing two samples, the data from which comes from
completely different groups of participants
One-Way ANOVA
F
num = k-1
denom = N-k
Comparing more than two samples, the data from which
comes from completely different groups of participants
Between-Subjects Two-Way ANOVA
F
---
Evaluating the effect of two independent variables, such
that each condition contains different participants
Within-Subjects Two-Way ANOVA
F
---
Evaluating the effect of two independent variables, such
that each condition contains the same participants
Mixed-Factorial ANOVA
F
---
Evaluating the effects of two independent variables, one
which is within-subjects, and the other between
Chi-Square Homogeneity
c2
k-1
Evaluating whether or not frequencies are equal
Chi-Square Goodness of Fit
c2
k-1
Evaluating whether or not a set of frequencies differs from
some comparison set of frequencies
Chi-Square Independence
c2
(R-1)(C-1)
Evaluating whether two sets of frequencies are different
according to two levels of categorization