Probability Sampling
Probability sampling involves selecting a sample from a population using randomization, ensuring each unit has a known chance of selection. It allows for reliable statistical inferences and minimizes bias, though it can be more complex and costly than non-probability sampling. Common methods include simple random sampling and systematic sampling, each with specific procedures and advantages.
- ▪Probability sampling relies on random selection, allowing for the calculation of selection probabilities and statistical inferences.
- ▪Simple random sampling gives every unit and sample an equal chance of being selected, often done without replacement for efficiency.
- ▪Systematic sampling selects units at fixed intervals after a random start, requiring a complete list of the population.
- ▪Simple random sampling is easy to implement when a full list exists but can be costly if the list must be created or if the sample is geographically dispersed.
- ▪The choice of probability sampling method aims to minimize sampling error and survey costs while considering operational constraints like frame characteristics.
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3.2 Sampling 3.2.2 Probability sampling Table of contents Text begins Topic navigation 3 Data gathering and processing 3.2 Sampling 3.2.1 Selection of a sample 3.2.2 Probability sampling 3.2.3 Non-probability sampling Probability sampling refers to the selection of a sample from a population, when this selection is based on the principle of randomization, that is, random selection or chance. Probability sampling is more complex, more time-consuming and usually more costly than non-probability sampling. However, because units from the population are randomly selected and each unit’s selection probability can be calculated, reliable estimates can be produced and statistical inferences can be made about the population.
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