Sample, Universe, Population

Sample

A sub-section of the population a representation of the population inference is generalized

Process of Sampling

1. Define the population
2. Develop Sampling Frame
3. Select a Samling Method
4. Determine sample size
5. Execute the sampling process

Sampling Population Sample

Sampling Techniques

1. Fixed vs sequential
2. Probability vs non-probability
3. Attributes vs

Probability Sampling Vs Non-probability Sampling

Probability	Non-probability
Every element has a chance of being in the sample	not equal chance
sample is random	sample is chosen by researcher according to their convenience
representative of the population	not representative

graph TB
A(Sampling Methods)  
A-->B(Probability)
A-->C(Non Probability)
B-->Z(Simple Random <br> Simple Stratified <br> Cluster <br> Systematic)
C-->X(Purposive <br> Snowball<br>Convenience)

Simple Random

Stratified Random Sampling

builds up from simple random divides the population into groups depending on characteristic. groups = stratas and then random sampling is performed (Each subject only one strata; different stratas can have different number of subjects)

Cluster Random Sample

A cluster is obtained by first dividing the population into randomly chosen sub-groups (clusters) a random assortment of clusters = the sample in stratified - there is no common characteristic needed

Systematic Sampling

Every k ^th element This method simply involves selecting participants at a set interval, starting from a random point.