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Simple Random Sampling
Where every sample has an equal chance of getting selected
How do you carry it out?
Get a sampling frame, usually a list of people or things.
Allocate each of them a unique number and choose a selection of these numbers at random.
Can be chosen through random number generator or through lottery sampling.
Ads of simple random sampliing
Free of bias
Easy and cheap to implement for small populations and samples
Each sampling unit has a known and equal chance of being chosen
Disads of simple random sampling
Not suitable when population size or sample size is large as it’s time consuming, expensive and disruptive.
A sampling frame is needed.
Systematic sampling
Sampling units are chosen at regular intervals from an ordered list
How do you carry it out?
Divide population by sample to find number you would continue taking from
E.g: if sample size is 20 and population is 100, 100/20=5 so you’d take every 5th unit.
Ads of Systematic sampling
Simple and quick to use
Suitable for large samples and large populations
Disads of Systematic sampling
A sampling frame is needed
It can introduce bias if the sampling frame is not random
Stratified Sampling
Population divided into mutually exclusive strata (males and females for e.g) and a random sample is taken from each
How do you carry out Stratified Sampling?
Split population into strata
The proportion of each strata should be the same
A simple formula can be used to calculate the number of people we should sample from each stratum:
no. of people sampled in stratum = (no. in stratum/ no. in population) x overall sample size
After collecting samples use random sampling
Ads of Stratified Sampling
Sample accurately reflects population strucutre
Guarantees proportional representation of groups within a population
Disads of Stratified Sampling
Population must be clearly classified into distinct strata
Selection within each stratum suffers fromt he same disadvantages as simple random sampling
Note 
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