1. 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

2. Which statistical test should I use?
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Which statistical test should I use?
Paired-samples
T-test
Independent samples
T-test
Are the groups composed of the sample people?
yes no
Are you counting up a number of events which have only one of two possible outcomes? no
yes
yes
Between-Subjects Two-Way ANOVA
Mixed-Factorial ANOVA
Sign test
Are you comparing a sample to a population?
Are you comparing two samples? no
yes no no
yes
Does each person experience only one condition?
Do you know the population standard deviation?
Are you comparing more than two samples?
Z-test
yes no
yes no
Between-Subjects One-Way ANOVA
One-Sample
t-test
Do you have more than one independent variable?
Are all the groups composed of the sample people?
yes no
Within-Subjects One-Way ANOVA
yes
yes
Are the groups composed of the sample people? no
Within-Subjects Two-Way ANOVA

3. Test Statistic df Use this test when: Sign Test N/A
Evaluating a series of repeated events, such that each event has one of two possible outcomes.
Z-test Z
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
Comparing two samples, the data from which comes from the same participants
Independent-Samples t-test
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
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Evaluating the effect of two independent variables, such that each condition contains different participants
Within-Subjects Two-Way ANOVA
Evaluating the effect of two independent variables, such that each condition contains the same participants
Mixed-Factorial ANOVA
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
Evaluating whether or not a set of frequencies differs from some comparison set of frequencies
Chi-Square Independence
(R-1)(C-1)
Evaluating whether two sets of frequencies are different according to two levels of categorization