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Population

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Entire group being studied

Portion of population selected for data

4 Methods of Sampling

Simple random sampling,
Cluster sampling,
Quota sampling
Stratified sampling.

Simple Random Sampling

Each member of the population is given an equal chance of being selected.
This could be done by using a computer program to randomly select sample from population.

Cluster Sampling

Breaks population down into non overlapping sub-groups called clusters. The entire population of a cluster that is randomly selected is then surveyed.
Clusters could be geographical areas.

This is a form of non-random sampling. The population is broken down into non-overlapping sub-groups. Quotas from each each sub-group are polled. Sub-groups could be 'male' and 'female'.

Stratified Sampling

A form of random sampling; the population is divided into non-overlapping subgroups and weighted based on demographic characteristics of population.

Data collected specifically for the problem or project at hand.

Data previously collected for any purpose other than the one at hand

Discrete numerical data

Possible values are isolated points along a number line. Values are either whole numbers or rounded off in some way. (e.g. shoe size, age in years)

Continuos numerical data

Values could technically be any number between upper and lower limits. (e.g. weight)

Nominal categorical data

Categorical data that cannot be ordered in any helpful way (e.g. nationality)

Ordinal categorical data

Categorical data that can be ordered in some way (Grades: A,B, C...)