Sampling and Types of sampling
In this blog I am going to go through something as simple as Sampling. Sampling is a statistical technique in which a subset of a population is taken or selected in such a way so as to represent the charecteristics of the entire population.
In general there are 5 different types of sampling
1. Simple
2. Cluster
3. Stratified
4. Convenience
5. Systematic
Simple
Simple random sampling (SRS) is a sampling technique in which we consider the entire population.
The thing about simple random sampling is that in the group that is taken each member of the population
has an equal chance of occurring. SRS then selects a member at random from the given population and forms
the sample that is a representative of the entire population.
Cluster Sampling
Cluster sampling is another sampling technique in which a cluster is chosen
at random and only those clusters are considered to represent the entire sample
while the other clusters are ignored. This is used in place of stratified sampling when the
dataset is huge.
Stratified Sampling
Stratified sampling is one of the best sampling methods.
In stratified sampling the data is divided into strata (groups based on the characteristics of the members).
From the strata that is available equal number of members are chosen from each group to form the sample.
This sampling technique better represents the population.
Convenience Sampling
Convenience sampling is one of the most dangerous sampling methods since there can be heavy bias in the sample that is being considered.
In convenience sampling only a selected group of data that is easily available to sample is considered.
Examples of this could be a survey that is being taken by a student on a specific topic and if the people they considered for the sampling were the members
if their class then such a sample can be called as a convenience sample. Most of the times the sample that is being obtained is totally biased and misleading.
Systematic Sampling
Systematic sampling in layman terms is a sampling technique in which a system or
a method is being followed to randomly pick a member from the entire population.
For instance picking every fifth member from the population from a total of 1000 members
is an example of a systematic population. Although this can be biased, this is better
than convenience sampling by all means.