Sampling Techniques

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What is a Random Sampling Technique?

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A method where each member of a population has an equal chance of being selected. Example: A pollster uses a computer to generate random numbers to select participants.

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Define Stratified Sampling.

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A technique where the population is divided into subgroups (strata) and random samples are taken from each stratum. Example: Selecting 500 people from each of 10 cities.

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What is Convenience Sampling?

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A non-probability sampling method where participants are selected based on their easy availability. Example: Interviewing colleagues who work in the same building.

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Explain Cluster Sampling.

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A sampling technique where the population is divided into clusters, and entire clusters are randomly selected. Example: Randomly selecting entire classrooms from a school.

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What is Systematic Sampling?

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A method where you select every nth member from a list of the population. Example: Selecting every 10th name from an alphabetical list of students.

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What is the purpose of Sampling Techniques?

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To obtain a representative subset of a population for analysis, making it easier and more cost-effective to gather data.

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What is the difference between Random and Stratified Sampling?

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Random sampling gives each individual an equal chance of selection, while stratified sampling ensures specific subgroups are represented.

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When is Stratified Sampling preferred?

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When researchers want to ensure that specific subgroups within a population are adequately represented in the sample.

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What is a potential drawback of Convenience Sampling?

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It may lead to biased results as it does not represent the entire population.

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How does Cluster Sampling work?

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The population is divided into clusters, and a random sample of these clusters is selected. All individuals within chosen clusters are then surveyed.