Random sampling methods

Random sampling <333

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