Differentiate between probability and non-probability sampling
The key differences between probability and non-probability sampling techniques lie in the way samples are selected and the ability to generalize the findings to the entire population.
Probability Sampling:
Random Selection: Every member of the population has a known, non-zero chance of being selected.
Unbiased: Since selection is random, there is less risk of bias in the sample.
Generalizable: Results can be generalized to the population because the sample is representative.
Examples: Simple random sampling, stratified sampling, cluster sampling, systematic sampling.
Use: Common in large-scale, scientific, or quantitative research where precision is required.
Non-Probability Sampling:
Non-Random Selection: Not all members of the population have a chance of being selected. Selection is based on factors like convenience or judgment.
Potential Bias: Since selection is not random, there is a higher risk of selection bias.
Limited Generalization: Results cannot reliably be generalized to the entire population.
Examples: Convenience sampling, judgmental (purposive) sampling, quota sampling, snowball sampling.
Use: Common in exploratory research, qualitative studies, or when time and resources are limited.
In short, probability sampling offers more reliable and representative results, while non-probability sampling is often quicker and more practical but less precise.
#How would you differentiate between probability and non-probability sampling techniques?
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