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How do you select a random sample from infinite universe/ population?Question
How do you select random sample from infinite universe or population?
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It is not possible to select a truly random sample from an infinite population or universe, as there is no way to access or evaluate every element within infinity. However, if we assume a large and practically infinite population, researchers often use statistical techniques to achieve a representative sample.
One approach is to use a random sampling method called "probability sampling." This involves assigning a probability of selection to each element in the population, thereby ensuring that every element has a chance of being selected. Examples of probability sampling methods include simple random sampling, stratified sampling, and cluster sampling.
For instance, in simple random sampling, each element in the population has an equal chance of being selected. This can be done by assigning a unique identification number to each element and using a random number generator to choose the desired sample size.
Stratified sampling involves dividing the population into homogeneous subgroups or strata and then selecting a random sample from each stratum proportionate to its size or importance.
Cluster sampling involves dividing the population into clusters (usually geographical or spatial) and then randomly selecting clusters to include in the sample. All elements within the selected clusters are then included in the sample.
It is important to note that even with these techniques, the sample will always be a finite representation of the infinite population, leading to some level of uncertainty or sampling error.
One approach is to use a random sampling method called "probability sampling." This involves assigning a probability of selection to each element in the population, thereby ensuring that every element has a chance of being selected. Examples of probability sampling methods include simple random sampling, stratified sampling, and cluster sampling.
For instance, in simple random sampling, each element in the population has an equal chance of being selected. This can be done by assigning a unique identification number to each element and using a random number generator to choose the desired sample size.
Stratified sampling involves dividing the population into homogeneous subgroups or strata and then selecting a random sample from each stratum proportionate to its size or importance.
Cluster sampling involves dividing the population into clusters (usually geographical or spatial) and then randomly selecting clusters to include in the sample. All elements within the selected clusters are then included in the sample.
It is important to note that even with these techniques, the sample will always be a finite representation of the infinite population, leading to some level of uncertainty or sampling error.
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