• Taking part from the whole population

• This sample will represent the whole population

• We would like to have them almost the same

• We can’t take the whole population because of time, money and ethical issue

• Taking small sample can tell us about the whole population

• When the sample you’re taken is different from the population you want  to infer your result to

• For example, having a sample from the neighborhood to infer the income of the neighborhood and by chance Bill Gates was sampled 

• Other real life examples: high school participant in chorionic illness  or sampling from  the first or last bench in the class

• Always make sure that your sample is representing and enough – that’s why we calculate the sample size

Probabilistic: Every member of the population has equal chance to be selected.

• Simple random Sampling

• Systematic Sampling 

• Stratified Sampling 

• Cluster Sampling

Non probabilistic: Every member of the population has No equal chance to be selected.

• Convenience Sampling

• Snowball Sampling 

Simple Random Sampling:

Assigning numbers to the individuals and then randomly choosing from those numbers through an automated process. Then, these chosen numbers are included in the sample/study.

We can do it by four ways: 

1. Choosing from Box

2. Coin (½ rial)

3. Random number table

4. Number generating software https://www.randomizer.org/

   Example: if I want to choose 20 participant from a population of 100

Systematic Sampling:

When you choose every individual in position of sequence  to be a part of the sample. For example, you can select every 5th person to be in the sample, you can select every 3rd person to be in the sample.