Probability sampling is based on the concept of random selection where each Simple Random Sampling elements have non-zero chance to be occurred as sample. Sampling techniques can be divided into two categories: probability sampling and non-probability sampling. Randomization or chance is the core of probability sampling techniques.
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For example, if a researcher is dealing with a population of 100 people, each person in the population would have the odds of 1 out of 100 for being chosen. This differs from non-probability sampling, in which each member of the population would not have the same odds of being selected.
Different types of probability sampling
Application of probability sampling
- In opinion poll, a relatively small number of persons are interviewed and their opinions on current issues are solicited in order to discover the attitude of the community as a whole.
- At border stations, customs officers enforce the laws by checking the effects of only a small number of travelers crossing the border.
- A departmental store wises to examine whether it is losing or gaining customers by drawing a sample from its lists of credit card holders by selecting every tenth name.
- In a manufacturing company, a quality control officer take one sample from every lot and if any sample is damage then he reject that lot.
- Creates samples that are highly representative of the population.
- Sampling bias is tens to zero.
- Higher level of reliability of research findings.
- Increased accuracy of sampling error estimation.
- The possibility to make inferences about the population.
- Higher complexity compared to non-probability sampling.
- More time consuming, especially when creating larger sample.
- Usually more expensive than non-probability sampling.
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