# 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  Simple Stratified  Cluster  Systematic)
C-->X(Purposive  SnowballConvenience)```

### 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.