Sampling Types

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What is a Simple Random Sample?

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A sampling method where every individual has an equal chance of being selected. For example, drawing names from a hat.

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What is a Systematic Sample?

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A sampling method where you select every nth individual from a list. For example, choosing every 10th student on a roster.

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What is a Cluster Sample?

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A sampling method where the population is divided into clusters, and entire clusters are randomly selected. For example, selecting all students from randomly chosen classrooms.

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What is a Stratified Sample?

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A sampling method where the population is divided into subgroups (strata) and random samples are taken from each stratum. For example, sampling students from different grades.

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What is the main advantage of using a Simple Random Sample?

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It minimizes bias and ensures that every individual has an equal opportunity to be included.

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What is a disadvantage of Systematic Sampling?

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If there is a hidden pattern in the population, it may lead to biased results.

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When is Cluster Sampling most useful?

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When the population is large and spread out, making it impractical to conduct a simple random sample.

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What is the purpose of Stratified Sampling?

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To ensure that specific subgroups are adequately represented in the sample.

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How does Simple Random Sampling differ from Stratified Sampling?

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Simple random sampling does not consider subgroups, while stratified sampling ensures representation from each subgroup.

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What is an example of a situation where Cluster Sampling would be appropriate?

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Conducting a survey in a large city by randomly selecting several neighborhoods and surveying all residents in those neighborhoods.