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Which sampling provides separate estimates for population means for different purposes and also an over all estimate?
- a)Multistage sampling
- b)simple random sampling
- c)systematic sampling
- d)stratified sampling
Correct answer is option 'D'. Can you explain this answer?
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Which sampling provides separate estimates for population means for di...
Understanding Stratified Sampling
Stratified sampling is a method that provides separate estimates for population means for different subgroups (strata) and also allows for an overall estimate of the population. Here’s how it works:
Definition of Stratified Sampling
- Stratified sampling involves dividing the population into distinct subgroups that share similar characteristics.
- Each subgroup, or stratum, is then sampled independently.
Advantages of Stratified Sampling
- Enhanced Precision: By ensuring that each subgroup is represented, stratified sampling reduces variability within each stratum, leading to more accurate estimates.
- Separate Estimates: It allows researchers to obtain separate estimates for each stratum, which can be crucial for understanding differences across diverse segments of the population.
Overall Estimate
- After obtaining the means from each stratum, researchers can easily compute a weighted average to derive an overall population estimate. This is done by considering the sizes of each stratum relative to the total population.
Comparison to Other Sampling Methods
- Multistage Sampling: Involves multiple stages of sampling but does not guarantee separate estimates for strata.
- Simple Random Sampling: Provides an overall estimate but lacks the ability to analyze subgroups distinctly.
- Systematic Sampling: Similar to simple random sampling but also does not account for separate subgroup estimates.
Conclusion
Stratified sampling is particularly advantageous when researchers aim to capture the diversity of a population and require both specific subgroup insights and a comprehensive overall estimate. This method ensures a thorough understanding and representation of the population's characteristics.
Stratified sampling is a method that provides separate estimates for population means for different subgroups (strata) and also allows for an overall estimate of the population. Here’s how it works:
Definition of Stratified Sampling
- Stratified sampling involves dividing the population into distinct subgroups that share similar characteristics.
- Each subgroup, or stratum, is then sampled independently.
Advantages of Stratified Sampling
- Enhanced Precision: By ensuring that each subgroup is represented, stratified sampling reduces variability within each stratum, leading to more accurate estimates.
- Separate Estimates: It allows researchers to obtain separate estimates for each stratum, which can be crucial for understanding differences across diverse segments of the population.
Overall Estimate
- After obtaining the means from each stratum, researchers can easily compute a weighted average to derive an overall population estimate. This is done by considering the sizes of each stratum relative to the total population.
Comparison to Other Sampling Methods
- Multistage Sampling: Involves multiple stages of sampling but does not guarantee separate estimates for strata.
- Simple Random Sampling: Provides an overall estimate but lacks the ability to analyze subgroups distinctly.
- Systematic Sampling: Similar to simple random sampling but also does not account for separate subgroup estimates.
Conclusion
Stratified sampling is particularly advantageous when researchers aim to capture the diversity of a population and require both specific subgroup insights and a comprehensive overall estimate. This method ensures a thorough understanding and representation of the population's characteristics.
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Which sampling provides separate estimates for population means for different purposes and also an over all estimate?a)Multistage samplingb)simple random samplingc)systematic samplingd)stratified samplingCorrect answer is option 'D'. Can you explain this answer?
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