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Questions and Answers
What is random sampling?
What is random sampling?
Random sampling is a statistical method used to estimate the characteristics of a population based on a sample of individuals or items.
What is the advantage of stratified random sampling over simple random sampling?
What is the advantage of stratified random sampling over simple random sampling?
It ensures that the sample is representative of the subgroups within the population.
How does systematic random sampling work?
How does systematic random sampling work?
It involves selecting every Nth member of the population, where N is a predetermined interval.
What is the main characteristic of simple random sampling?
What is the main characteristic of simple random sampling?
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What is the potential drawback of simple random sampling for large populations?
What is the potential drawback of simple random sampling for large populations?
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Why is stratified random sampling more efficient than simple random sampling for large populations?
Why is stratified random sampling more efficient than simple random sampling for large populations?
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Study Notes
Methods of Random Sampling
Random sampling is a statistical method used to estimate the characteristics of a population based on a sample of individuals or items. It involves selecting a subset of the population randomly, ensuring that each member of the population has an equal chance of being selected. There are several methods of random sampling, including simple random sampling, stratified random sampling, and systematic random sampling.
Simple Random Sampling
In simple random sampling, every member of the population has an equal chance of being selected for the sample. This method is unbiased and ensures that the sample is representative of the population. However, it can be time-consuming and may not be suitable for large populations.
Stratified Random Sampling
Stratified random sampling involves dividing the population into homogeneous subgroups (strata) based on certain characteristics, and then randomly selecting individuals from each stratum. This method ensures that the sample is representative of the subgroups within the population, and it can be more efficient than simple random sampling for large populations.
Systematic Random Sampling
Systematic random sampling involves selecting every Nth member of the population, where N is a predetermined interval. This method is easy to implement and can be efficient for large populations. However, it can introduce bias if the interval N is not chosen randomly or if there is a pattern in the data.
In summary, random sampling is a useful statistical method for estimating the characteristics of a population. There are three main methods of random sampling: simple random sampling, stratified random sampling, and systematic random sampling. Each method has its advantages and disadvantages, and the choice of method depends on the characteristics of the population and the research objectives.
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Description
Learn about the statistical method of random sampling, which is used to estimate population characteristics based on a subset. Discover the three main methods: simple random sampling, stratified random sampling, and systematic random sampling, each with its own advantages and disadvantages.
