Random Sampling and its types , Advantages of Sample survey over Full Counts , Population Vs Sample and Parameter vs Statistic

• Population - It means totality or universe of units , examine each units of the population . 
• Sample can be used to find out something about a certain characteristic of a population by examine a representative fraction of the population.
• Parameter is some characteristic of the population whose value is unknown . 
• Statistic is some characteristic of the population whose value is known and can be calculated . 

Advantages of Sample Surveys over Full counts : 

1. Practicability : It is very low practical to conduct a full count like done in a census but it is very easy to conduct a sample survey  
2. Speed : It will take time to conduct a full count whereas speed is very high in sample survey 
3. Cost: It will take more cost to cover each member of population . Data collectors are called enumerator. They will be paid more for a full count rather than a sample survey. 
4. Accuracy : Sample surveys have very high accuracy because data collectors approach less people . 

Random Sampling 

In Random Sampling , you start with a complete sample frame of all eligible individuals that have an equal chance to be a part of the selected sample. The selection must occur in a random way meaning that they do not differ in any significant way from observations not sampled. 

They are of four types : 

1. Simple random sampling 

2. Stratified Random Sampling 

3. Cluster Sampling 

4. Multi Stage sampling 

Simple Random Sampling :- 

An example of a simple random sample would be the names of 50 workers being chosen out of a shop company of 500 workers. In this case the population is all 500 workers , and the sample is random because each worker has an equal chance of being chosen. Random sampling is used to conduct random control tests or for blinded experiments. 

Stratified Sampling :- 

This technique divides the elements of the population into small subgroups(strata) based on the similarity in such a way that the elements within the group are homogeneous and heterogenous among the other subgroups formed and then the elements are randomly selected from each of these strata.

 

Cluster Sampling :- 

Our entire population is divided into clusters or sections and then the clusters are randomly selected. All the elements of the cluster are used for sampling. Clusters are identified using details such as age, sex, location , etc. 

Multi-Stage Sampling :- 

It is the combination of one ore more methods described above. Population is divided into multiple clusters and then these clusters are further divided and grouped into various sub groups based on similarity . One or more clusters can be randomly selected from each stratum. This process continues until the cluster can't be divided anymore. For example- Country can be divided into states , cities, urban and rural and all the areas with similar characteristics can be merged to form a strata.