Yr 10 S1 Data - Stratified Sampling

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What is stratified sampling?

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Stratified sampling is a method of sampling that involves dividing a population into subgroups (strata) based on shared characteristics, and then taking a sample from each stratum to ensure representation.

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Why is stratified sampling used?

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Stratified sampling is used to ensure that specific subgroups within a population are adequately represented in the sample, which can lead to more accurate and reliable results.

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How do you determine the size of each stratum in a stratified sample?

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The size of each stratum in a stratified sample is determined based on the proportion of that stratum in the overall population.

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If a school has 300 students, with 150 boys and 150 girls, how many boys should be selected in a stratified sample of 60 students?

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30 boys should be selected, as they represent 50% of the population.

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What is the formula for calculating the number of samples needed from each stratum?

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The formula is: (Size of stratum / Total population size) x Total sample size.

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In a stratified sample of 200 students, if there are 80 boys and 120 girls, how many boys should be selected?

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80 boys should be selected, as they represent 40% of the population.

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What is the importance of sample size in stratified sampling?

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Sample size is important in stratified sampling because it affects the accuracy and reliability of the results; larger samples generally provide more reliable estimates.

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How can stratified sampling reduce sampling bias?

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Stratified sampling reduces sampling bias by ensuring that all subgroups are represented in the sample, which helps to avoid overrepresentation or underrepresentation of any group.

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What is a potential drawback of stratified sampling?

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A potential drawback of stratified sampling is that it can be more complex and time-consuming to implement compared to simple random sampling.

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How does stratified sampling differ from simple random sampling?

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Stratified sampling involves dividing the population into strata and sampling from each, while simple random sampling involves selecting individuals randomly from the entire population without regard to subgroups.