Distinguish between Random sampling & Deliberate sampling.
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What is the difference between a simple random sample and a stratified random sample?
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BY MARY HALL
Updated Feb 8, 2018
Simple random samples and stratified random samples differ in how the sample is drawn from the overall population of data. Simple random samples involve the random selection of data from the entire population so each possible sample is equally likely to occur. In contrast, stratified random sampling divides the population into smaller groups, or strata, based on shared characteristics. A random sample is taken from each stratum in direct proportion to the size of the stratum compared to the population. The sample subsets are then combined to create a random sample.
Simple random sampling and stratified sampling are both types of probability sampling where each sample has a known probability of being selected. This is different from judgmental sampling, where the units to be sampled are handpicked by the researcher.
The population is the total set of observations or data. A sample is a set of observations from the population. The sampling method is the process used to pull samples from the population. A simple random sample is a random sample pulled from the entire population with no constraints placed on how the sample is pulled. This method has no bias in selecting the sample from the population, so each population element has an equal chance of being included in the sample.
What is the difference between a simple random sample and a stratified random sample?
BY MARY HALL
Updated Feb 8, 2018
Simple random samples and stratified random samples differ in how the sample is drawn from the overall population of data. Simple random samples involve the random selection of data from the entire population so each possible sample is equally likely to occur. In contrast, stratified random sampling divides the population into smaller groups, or strata, based on shared characteristics. A random sample is taken from each stratum in direct proportion to the size of the stratum compared to the population. The sample subsets are then combined to create a random sample.
Simple random sampling and stratified sampling are both types of probability sampling where each sample has a known probability of being selected. This is different from judgmental sampling, where the units to be sampled are handpicked by the researcher.
The population is the total set of observations or data. A sample is a set of observations from the population. The sampling method is the process used to pull samples from the population. A simple random sample is a random sample pulled from the entire population with no constraints placed on how the sample is pulled. This method has no bias in selecting the sample from the population, so each population element has an equal chance of being included in the sample.
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The difference between Random sampling & Deliberate sampling can be explained as follows:
Random sampling:
- Meaning: It refers to the probability sampling where the sample is selected randomly.
- Element of biasness: There is no biasness under this method as every sample unit is being given equal chance of selection.
Deliberate sampling:
- Meaning: It refers to the non probability sampling where the sample is selected on the basis of the purpose of the study.
- Element of biasness: There is biasness under this method as every sample unit is not being given equal chance of selection.
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