Cheatography
https://cheatography.com
Overview of statistical tests an when to use them
This is a draft cheat sheet. It is a work in progress and is not finished yet.
1 Dependent Variable & 0 IVs (1 Population)
DV 
Test 
interval & normal 
Onesample ttest 

tests if a sample mean differs sig. from a hypothesized value 
ordinal or interval 
Onesample median test 

tests if a sample median differs sig. from a hypothesized value 
categorial (2 categories) 
Binominal Test 

tests if the proportion of successes on a twolevel categorial dependent variable differs sig. from a hypothesized value 
categorial 
Chisquare goodnessoffit 

tests if the observed proportions for a categorial variable differ from hypothesized proportions 
1 DV & 1IV with 2 levels (independent groups)
DV 
Test 
interval & normal 
2 independent sample ttest 

compares the means of a normally distributed interval DV for two independent groups 
ordinal or interval 
WilcoxonMann Whitney test 

is a nonparametric analog to the independent samples ttest 

used, when you do not assume that the DV is a normally distributed interval variable 
categorial 
Chisquare test 

to see if there is a relationship between 2 categorial varibales 

assumes that each cell has an expected frequency of 5 or more 
categorial 
Fischer`s exact test 

same as Chisquare test, but can be used regardless of the expected frequency (expected frequency of 5 or less) 
1 DV & 1IV with 2 or more levels (indep. groups)
DV 
Test 
interval & normal 
OneWay ANOVA 

test for differences in the means of the DV broken down by the levels of the IV 

used when categorial IV (with one or more categories) an normally distributed interval DV 
ordinal or interval 
Kruskal Wallis test 

is nonparametric version of ANOVA and a generalized form of the MannWhitney test since it permits two or more groups 
categorial 
Chisquare test 
1 DV & 1IV with 2 or more levels (indep. groups)
DV 
Test 
interval & normal 
OneWay ANOVA 

test for differences in the means of the DV broken down by the levels of the IV 

used when categorial IV (with one or more categories) an normally distributed interval DV 
ordinal or interval 
Kruskal Wallis test 

is nonparametric version of ANOVA and a generalized form of the MannWhitney test since it permits two or more groups 
categorial 
Chisquare test 


1 DV & 1IV with 2 (dependent/matched groups)
DV 
Test 
interval & normal 
Paired ttest 

used when you have two related observations and want to see if the means on these two normally distributed interval variables differ from one another 
ordinal or interval 
Wilcoxon signed rank sum test 

is nonparametric version of a paired sample ttest 

used, when you do not wish to assume that the difference between the two variables is the interval and normally distributed 
categorial 
McNemar test 

use if interested in the marginal frequencies of two binary outcomes 
1 DV & 1 IV with 2 or m. lev. (dep./matched g.)
DV 
Test 
interval & normal 
OneWay repeated measures ANOVA 

is the equivalent of paired ttest, but allows for 2 or more levels of the categorial variable 
ordinal or interval 
Friedman test 

use when you have one withinsubjects IV with 2 or more levels and a DV that is not interval or normally distributed 
categorial (2 categories) 
Repeated measures logistic regression 

use if you have a binary outcome measured repeatedly for each subject and wish to run a logistic regression that accounts for the effects of multiple measures from a single subject 
1 DV & 2 or more IVs (indepen. groups)
DV 
Test 
interval & normal 
factorial ANOVA 

use if you have 2 or more categorial IV and a single normally distributed interval DV 
ordinal or interval 
Ordered logistic regression 

used, when the DV is ordered, but not continuous 
categorial (2 categories 
Factorial logistic regression 

used, when you have 2 or more categorial IV but a dichotomous DV 
1 DV & 1 interval IV
DV 
Test 
interval & normal 
Correlation 

used, when you want to see the relationship between two (or more) normally distributed interval variables 
interval & normal 
Simple linear regression 

allows us to look at the linear relationship between one normally distributed interval IV and one normally distributed interval DV 
ordinal or interval 
Nonparametric correlation (Spearman) 

used, when one or both of the variables are not assumed to be normally distributed and interval 

the values of the variables are converted in ranks and then correlated 
categorial 
Simple logistic regression 

assumes that the outcome variable is binary 
1 DV & 1 or m. interval IV/ 1 or m. categ. IVs
DV 
Test 
interval & normal 
Multiple Regression 

similar to simple regression, except that in multiple regression you have more that one IV in the equation 
interval & normal 
Analysis of Covariance 

like ANOVA, except in addition to the categorial IV you also have continuous IV 
categorial 
Multiple logistic regression 

like simple regression, except that there are 2 or more IV 

IV can be dummy or interval variables, but cannot be categorial variables (if, should be coded into 1 or more dummy variables) 
categorial 
Discriminant analysis 

used, when you have one or more normally distributed interval IV and a categorial DV 

is a multivariate technique that considers the latent dimensions in the IV for predicting group membership in the categorial DV 


2+ DV & 1 IV with 2 or more levels (indep. groups)
DV 
Test 
interval & normal 
Oneway MANOVA 

like ANOVA, except that there are 2 or more DV. 

there is one categorial IV and two or more DV 
interval & normal 
Multivariate multiple linear regression 

used, when you have two or more DV that are to be predicted from two or more IV 
interval & normal 
Factor analysis 

is a form of exploratory multivariate analysis that is used to either reduce the number of variables in a model or to detect relationships amongst variables 

all variabales need to be interval and assumed to be normally distributed 

goal is to try to identify factors which underlie the variables 
2 sets of 2+ DV & 0 IV
DV 
Test 
interval & normal 
Canonical correlation 

is a multivariate technique used to examine the relationship between two groups of variables 

for each set of variables, it creates latent variables and looks at the relationship among the latent variables 

assumes that all variables in the model are interval and normally distributed 
