### Inferential Statistics

Used to draw informed guesses (inferences) about situations where we only have part of the total information (ie a sample from a population).

These are commonly used to analyse the results of an experiment.

A sample is selected to try and test a statistical theory about the whole population.

These methods allow you to compare the measurements you have collected from one sample with another sample (or population) and allows you to make a judgement on how similar the samples are.

All these tests make certain assumptions about both the nature of your data and how it was collected.

For example you have to be clear what the __measurement__ of your data is (nominal, ordinal, interval or ratio)

#### Multi-Variate Methods of Analysis

These are used to explore the inter-relationships among three or more variables simultaneously.

Examples include multiple regression, cluster analysis and factor analysis

#### Quantitative Analysis

The purpose of analysis is to seek explanation and understanding.

Just because two variables of which you have measurements appear to be related, this does not mean that they are related.

Statistical association between two variables may be a matter of chance, or due, to the effect of some third variable.

You also have to find (or suggest) a mechanism that links the variables together.

Measures of Central Tendency

Measures of Dispersion

Correlation

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