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Deciding which is the right test to use in a given situation is one of the hardest things to master.
These signposts should help...
Interval / Ratio data? Normally distributed? 
Ordinal data? Nonnormal? Ranked? 

Are the samples independent of each other, i.e.unpaired, unmatched ? 
Independent sample (student's) 't'test 
MannWhitney 'U'test for medians 
Are the samples dependent upon each othe, matched / paired? 
Paired 't'test 
Wilcoxon matched pairs test 
Then we must simply look for an association between the frequency distributions because there is no mean or median to find...
>
2 x 2 contingency table

2
x 2 contingency table

Chi square test 
Yates' correction 
If the data set is small and if three or more factors are involved, the G test becomes more preferable than Chi
A
test of association for three or more factors?

The
G test

Do you have just one data set and want to test it against a standard normal distribution (use 1)
Or do you want to compare two sets to see if they both have the same distribution / come from the same population? (use 2)
1) Your data against a standard normal distribution? 
2) Two sets of data against each other 
KolmogorovSmirnov one sample test for normality 
Twosample KolmogorovSmirnov test 
*Use Correlation:
Is your data on the Ordinal scale (Nonparametric) 
Is your data on the Interval or Ratio scale? (Parametric) 
Spearman's rank correlation coefficient test 
Pearson's product moment correlation coefficient test (PPMCC) 
Is the data Parametric?
Is there no more than one dependent variable?
Have you established that the relationship is linear or can be transformed to become linear?
*Use Regression:
Are you interested in estimating the probable value of one variable given a value of the other? 
Are you working with at least two independent variables and one dependent variable 
Use simple Linear regression 
Use multiple regression 
Is there just one dependent variable under investigation?
* Use ANOVA's
Does each subject provide just one score?
*Use Independent groups ANOVA's
How many sources of influence are you investigating?
1
source of influence

2
(or more) sources of influence

Use
1factor ANOVA

Use
2factor ANOVA's

Are you trying to deal with two or more dependent variables?
Only 1 dependent variable 
2 (or more) dependent variables 
Use ANOVA 
Use MANOVA 
see above 
Does each subject supply more than one score each i.e. repeated measures?
This can often be measures over time
*Use repeatedmeasure ANOVA's
Do you suspect that there are other variables (must be measurable) that are influencing your results ?
*Use ANCOVA
Use repeated measures ANOVA 
Use ANalysis of COVAriance 
*Consider one of the following multivariate analysis options.
Are you interested in exploring the influence of several variables simultaneously?
Do you alraedy have grouping in existence?
No: use Principal Components analysis
Yes: use Discriminant analysis
Do you wish to explore relationships between objects based upon comparisons (similarities and differences) of their visible features?
Use Cluster analysis
No a priori groupings 
a priori groupings exist 
visible features measurable? 
Use Principal Components Analysis 
Use Discriminant Analysis 
Use Cluster analysis 
Do you wish to investigate whether the objects are geographically clustered together, evenly or randomly dispersed? can you use dendrograms?
Use Nearest Neighbour analysis
Are you conducting an experiment looking at the distribution of a group of objects (e.g. a plant species) within an experimental area. Do you wish to know if their distribution is random?
Use Point pattern analysis.
A quick 'field method' to plot heights within an experimental plot...
Use Trend surface analysis
Geographical distribution of objects 
Distribution of objects within an experimental plot 
Mapping your experimental plot 
Nearest Neighbour analysis 
Point pattern analysis 
Trend surface analysis 
End of signpost page