It is extremely difficult or even impossible to recruit the whole population for an experiment. If this was possible, it would be the perfect sample for any study. Since this is the case, statisticians decided that sampling can be the solution for this conundrum under specific conditions including sample size and randomization i.e. random sampling techniques. Today, I’m going to elaborate on random sampling techniques in an easy way -hopefully-.
The main idea behind random sampling is to eliminate bias.The types of random sampling techniques are:
- Simple random sampling.
- Systematic random sampling.
- Stratified random sampling.
- Cluster random sampling.
Let’s go into details in each of random sampling techniques.
1. Simple randomization
- Give each participant a numb>er from 1 to 30.
- https://www.randomizer.org, we can follow these easy steps and get a simple random sample as shown in the following illustrations.
Looking at the above photo, we are presented with a sample of (30) divided into two groups and each group containing (15) participants. The participants in group one are numbered (1،2،4،7،11،13،14،17،18،21،24،25،26،28،29).
2. Systematic randomization:
To use this method, we chose to recruit every third individual coming the clinic, so we skip 1 and 2 and take 3, then we skip 4 and 5 and we take 6, and so on until we reach the number of participants we need.
3. Cluster randomization:
Let’s assume that we have a list of hospitals participating in our study but we don’t have a list of potential individuals. Assuming we had (4) hospitals in our list, so we decided to select the first and the third hospitals, then we have to recruit all individuals in these two selected hospitals in our sample.
This is a brief explanation of this important matter. Please have a look at the photo at the top for better understanding. You can read the Arabic version of this article here.