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 Signpost page

Deciding which is the right test to use in a given situation is one of the hardest things to master.

These signposts should help...

# Are you trying to find the differences (mean, median etc) between two groups; both measuring the same variable?

 Interval / Ratio data? Normally distributed? Ordinal data? Non-normal? Ranked? Are the samples independent of each other, i.e.unpaired, unmatched ? Independent sample (student's) 't'-test Go to page Mann-Whitney 'U'-test for medians Go to page Are the samples dependent upon each othe, matched / paired? Paired 't'-test Go to page Wilcoxon matched pairs test Go to page

# Is your data only on the Nominal scale?

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 Go to page Yates' correction Go to page

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 Go to page

# Are you dealing with distributions that have data only on the Ordinal scale?

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 Kolmogorov-Smirnov one sample test for normality Go to page Two-sample Kolmogorov-Smirnov test Go to page

# Are you trying to assess the strength of the relationship between the values of two variables?

*Use Correlation:

 Is your data on the Ordinal scale (Non-parametric) Is your data on the Interval or Ratio scale? (Parametric) Spearman's rank correlation coefficient test Pearson's product moment correlation coefficient test (PPMCC) Go to page Go to page

# Are you trying to establish the precice mathematical relationship (i.e. the nature of the relationship) between two variables?

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 Go to page Go to page

# Are you investigating the differences between three or more samples?

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 1-factor ANOVA Use 2-factor ANOVA's Go to page Go to page

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 Go to page

Does each subject supply more than one score each i.e. repeated measures?

This can often be measures over time

*Use repeated-measure 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 Go to page Go to page

# Are you attempting to reduce a large dataset and at the same time, look for patterns within the set?

*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 Go to page Go to page Go to page

# Are you interested in the spatial distribution of your objects?

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

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