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What is a stratified random sample?
Stratified random sampling is a sampling scheme which is used when the population comprises a number of strata, or subsets, which are similar within the strata but differ from one stratum to another. One example is school children stratified according to classes, or salaries stratified by departments.A simple random sample may not have enough representatives from each stratum and the solution is to use stratified random sampling. Under this scheme, the overall sampling proportion (sample size/population size) is determined and a sample is drawn from each stratum which represents the same proportion.
What is stratified random sampling in statistics?
Stratified Random Sampling: obtained by separating the population into mutually exclusive (only belong to one set) sets, or stratas, and then drawing simple random samples (a sample selected in a way that every possible sample with the same number of observation is equally likely to be chosen) from each stratum.
What is the difference between stratified random sampling and cluster sampling?
Basically in a stratified sampling procedure, the population is first partitioned into disjoint classes (the strata) which together are exhaustive. Thus each population element should be within one and only one stratum. Then a simple random sample is taken from each stratum, the sampling effort may either be a proportional allocation (each simple random sample would contain an amount of variates from a stratum which is proportional to the size of that stratum) or according to optimal allocation, where the target is to have a final sample with the minimum variabilty possible. The main difference between stratified and cluster sampling is that in stratified sampling all the strata need to be sampled. In cluster sampling one proceeds by first selecting a number of clusters at random and then sampling each cluster or conduct a census of each cluster. But usually not all clusters would be included.
Which one is called non probability sampling a. cluster sampling b.quota sampling c. systematic sampling d. stratified sampling?
Answer is Quota sampling. Its one of the method of non-probability sampling.
What will be the sampling distribution of the mean for a sample size of one?
It will be the same as the distribution of the random variable itself.
What is a stratified random sample?
Stratified random sampling is a sampling scheme which is used when the population comprises a number of strata, or subsets, which are similar within the strata but differ from one stratum to another. One example is school children stratified according to classes, or salaries stratified by departments.A simple random sample may not have enough representatives from each stratum and the solution is to use stratified random sampling. Under this scheme, the overall sampling proportion (sample size/population size) is determined and a sample is drawn from each stratum which represents the same proportion.
What is stratified random sampling in statistics?
Stratified Random Sampling: obtained by separating the population into mutually exclusive (only belong to one set) sets, or stratas, and then drawing simple random samples (a sample selected in a way that every possible sample with the same number of observation is equally likely to be chosen) from each stratum.
What is the difference between stratified random sampling and cluster sampling?
Basically in a stratified sampling procedure, the population is first partitioned into disjoint classes (the strata) which together are exhaustive. Thus each population element should be within one and only one stratum. Then a simple random sample is taken from each stratum, the sampling effort may either be a proportional allocation (each simple random sample would contain an amount of variates from a stratum which is proportional to the size of that stratum) or according to optimal allocation, where the target is to have a final sample with the minimum variabilty possible. The main difference between stratified and cluster sampling is that in stratified sampling all the strata need to be sampled. In cluster sampling one proceeds by first selecting a number of clusters at random and then sampling each cluster or conduct a census of each cluster. But usually not all clusters would be included.
Which sampling method is appropriate to study the pervalence of AIDS among male and female in india in 1976 1986 1996 2006?
cluster sampling, quota sampling, systematic sampling, stratified random sampling which one is correct?
What is the difference between cluster sampling and stratified sampling?
Basically in a stratified sampling procedure, the population is first partitioned into disjoint classes (the strata) which together are exhaustive. Thus each population element should be within one and only one stratum. Then a simple random sample is taken from each stratum, the sampling effort may either be a proportional allocation (each simple random sample would contain an amount of variates from a stratum which is proportional to the size of that stratum) or according to optimal allocation, where the target is to have a final sample with the minimum variabilty possible. The main difference between stratified and cluster sampling is that in stratified sampling all the strata need to be sampled. In cluster sampling one proceeds by first selecting a number of clusters at random and then sampling each cluster or conduct a census of each cluster. But usually not all clusters would be included.
Which one is called non probability sampling a. cluster sampling b.quota sampling c. systematic sampling d. stratified sampling?
Answer is Quota sampling. Its one of the method of non-probability sampling.
What will be the sampling distribution of the mean for a sample size of one?
It will be the same as the distribution of the random variable itself.
Advantages of stratified sampling?
There are many advantages of stratified sampling. These include:Your sample better represents the population and so it is easier to generalise.There is little likelihood of a freak sampleAs you take people from all the different segments of the population, you can be sure that any results will not be due to something specific to one segment.
What are the 4 kinds of sampling methods in math?
random sampling ,systematic sampling , self-selected , and there is one more i don't know
What is the difference between probability sampling and simple random sampling?
Simple random sampling = A process of selecting subjects in such a way that each member of the population has an equal likelihood of being selected; you can throw all your subjects into a hat and draw them out one by one, or assign each member a number and choose every fifth number to be a participant.Probability sampling=A sampling procedure in which the probability that each element of the population will be included in the sample can be specified; you have a specific number of subjects and you know that they have a 50/50 chance of being chosen, or because of an anomaly, they may only have a 20/100 chance of being chosen for the experiment.*Your teacher is being tricky however, because there are 4 basic types of Probability sampling and simple random sampling is one of them. Also are stratified, systematic and cluster sampling. All four fall under the general title of Probability Sampling (P.S.)!! P.S. is kinda like the category and the 4 types are just different ways to do the sample, each has their own "little differences" in how the data is collected and assigned.
What is one way of ensuring that a sample is representative of the population?
One effective way to ensure that a sample is representative of the population is to use random sampling. This method involves selecting individuals from the population in such a way that every member has an equal chance of being chosen, thereby minimizing selection bias. Additionally, stratified sampling can be employed, where the population is divided into subgroups (strata) based on specific characteristics, and random samples are drawn from each stratum to reflect the population's diversity.
Which sampling is obtained by dividing the population into groups and selecting all individuals from within a random sample of the groups?
It is called one-stage cluster sampling. If random samples are taken within the selected clusters then it is two-stage cluster sampling.
