Random sampling VS non-random sampling techniques

Random sampling and non-random sampling are two methods used in research to select participants or observations from a population.

Random sampling is a statistical method of selecting a sample from a larger population in which each member of the population has an equal chance of being chosen. This method eliminates the possibility of bias and allows researchers to make inferences about the population from the sample. There are different types of random sampling techniques such as simple random sampling, stratified random sampling, cluster random sampling, and systematic random sampling.

For example, suppose a researcher wants to study the height of all the students in a particular school. In that case, they could use simple random sampling to randomly select a certain number of students from the population of all students in the school. This would give the researcher a representative sample of the population, and they could make generalizations about the height of all students in the school.

Non-random sampling is a method of selecting participants or observations from a population that does not involve random selection. Non-random sampling is often used when the population is too large, when it is difficult to obtain a random sample, or when a researcher wants to study a specific subgroup of the population. There are different types of non-random sampling techniques such as convenience, purposive, snowball, quota, and judgmental.

For example, if a researcher wants to study the experiences of individuals with a specific medical condition, they may use purposive sampling to select participants who have that condition. The researcher would identify individuals with the condition and invite them to participate in the study. This method allows the researcher to focus on the subgroup of interest, but it may only be representative of some of the population.