Difference between Random Sampling and Non-random Sampling
There are primarily two sampling methods: random sampling and non-random sampling. Random sampling is a sampling strategy in which each sample is chosen with equal probability.
The randomly selected sample represents an impartial representation of the entire population. If the sample picked does not accurately represent the population, it results in a sampling error.
Non-random sampling is a method of selecting a sample that is not only based on random chance, but rather on additional considerations. Non-random sampling is inherently skewed.
In this case, the sample will be chosen according to the researcher’s convenience, expertise, or judgment.
The following points outline the distinctions between random sampling and non-random sampling.
Differences between Random Sampling and Non-random Sampling
Aspect | Random Sampling | Non-random Sampling |
---|---|---|
Definition | A sampling strategy where each sample has an equal probability of being chosen. | A sampling method not based on random chance, but on criteria like convenience. |
Selection Process | Each member of the population has an equal chance of being selected. | Sample selection is based on the researcher’s convenience, judgment, or expertise. |
Sampling Biases | Inherently unbiased, aiming to represent the entire population fairly. | Exhibits inherent bias due to non-random selection criteria. |
Population Representation | Provides an impartial and representative sample of the entire population. | May not represent the full population comprehensively. |
According to | Based on the principles of probability. | Determination is made considering non-random criteria like convenience or judgment. |
Probability of Absolute Zero | Not applicable; each event has a non-zero probability. | It is possible for an event to have a probability of zero in some cases. |
Complexity | Simple and straightforward sampling technique. | More intricate and complex, requiring judgment and expertise for selection. |
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