What do you understand by random sampling? Why is it so useful? How does stratified sampling work and when can a researcher use it? Explain.

Question: What do you understand by random sampling? Why is it so useful? How does stratified sampling work and when can a researcher use it? Explain.

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Answer

Random sampling is a statistical technique used in research to select a representative sample of participants from a larger population. In random sampling, each member of the population has an equal chance of being selected for the study. The selection is made through a random process, such as drawing names out of a hat or using a random number generator.

Random sampling is useful because it ensures that the sample is representative of the population and reduces the likelihood of bias in the sample selection. It also allows for the application of statistical inference techniques, which enable researchers to draw conclusions about the population based on the results from the sample.

Stratified sampling is a sampling technique that involves dividing the population into subgroups, or strata, based on certain characteristics, such as age, gender, income, or education level. The sample is then selected by randomly selecting participants from each subgroup in proportion to their representation in the population.

Stratified sampling is useful when the researcher wants to ensure that the sample represents the population's diversity in terms of the characteristics of interest. It can also increase the precision of the estimates by reducing the variability within the strata. Stratified sampling can be used when the population is heterogeneous, and the researcher wants to ensure that the sample represents the population's different subgroups accurately.

For example, if a researcher wants to study the job satisfaction levels of employees in a large company, they could use stratified sampling by dividing the employees into subgroups based on their departments or job positions. They could then randomly select participants from each subgroup in proportion to their representation in the company's workforce. This would ensure that the sample represents the diversity of the company's workforce and increase the precision of the results.

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