Types Of Sampling Flashcards
Census sampling
A collection of every data point within a population to give an accurate view
- Asking everyone their opinions through a large-scale.
- time consuming
- expensive
- can be unfeasible
e.g. ONS survey for basket of goods.
Simple random sampling
- When every member of a population has an equal probability of being chosen
- often uses a number generator
Pros:
- no bias
Cons:
- risk of missing certain stratified groups
- not all opinions are included
Systematic sampling
- find a sample size n from the population and select people at intervals
Pros: - less bias
- in some cases, you can see changes across areas
Cons:
- requires a sampling framework
Parameter
A number that describes the whole population
E.g. mean of population is a parameter
Statistic
A number taken from a simple sample that describes it
- the mean of a sample
Sample
Taking a certain number of points from the population. This is less time-consuming and cheaper than a census but can be misleading
Population
Everything included that can be sampled
Stratified sampling
- the population is split up into different subsets and a proportional representation of each subset is chosen when compared to their total amount in the population.
Opportunity sampling
Takes samples of the population until a sample of the desired size has been achieved
Quota sampling
- the population is split into subsets and members are chosen until each quota is fullfilled
Qualitative data
Worded not numbers (opinions and descriptions)
Quantitative
Data given using numbers to count or measure something
Discrete data
- refers to specific values which people can be included into
Continuous data
- quantitative data that needs to be measured
- exists over a range of infinite values
Sampling frame
A list of all members of the population used to select certain members to sample
