Types of Sampling

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What is a Random Sample?

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A Random Sample is a sampling method where every individual in a population has an equal chance of being selected. This method helps to eliminate bias in the selection process.

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What is a Cluster Sample?

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A Cluster Sample is a sampling technique where the population is divided into groups (clusters), and entire clusters are randomly selected for the sample. This method is often used when populations are large and spread out.

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What is a Convenience Sample?

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A Convenience Sample is a non-probability sampling method where the sample is taken from a group that is easily accessible. This method may introduce bias as it does not represent the entire population.

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How does a Random Sample differ from a Convenience Sample?

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A Random Sample ensures that every individual has an equal chance of selection, while a Convenience Sample relies on easy access to participants, which may not represent the entire population.

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What is the purpose of using a Cluster Sample in research?

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The purpose of using a Cluster Sample is to simplify the sampling process by selecting entire groups rather than individuals, making it more practical and cost-effective, especially in large populations.

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What are the advantages of using a Random Sample?

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Advantages of a Random Sample include reduced bias, increased representativeness of the population, and the ability to generalize findings to the larger population.

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What are the disadvantages of a Convenience Sample?

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Disadvantages of a Convenience Sample include potential bias, lack of representativeness, and limited ability to generalize results to the entire population.

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In what scenario would a Cluster Sample be most appropriate?

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A Cluster Sample is most appropriate when the population is large and geographically dispersed, making it impractical to conduct a simple random sample.

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What is the main characteristic of a Stratified Sample?

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A Stratified Sample involves dividing the population into subgroups (strata) based on shared characteristics and then randomly sampling from each stratum to ensure representation.

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How can sampling bias affect research results?

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Sampling bias can lead to inaccurate conclusions, as the sample may not accurately represent the population, resulting in skewed data and unreliable findings.