# Probability sampling

Probability sampling | |
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**Probability sampling** is a selection of elements from the total population for a statistical sample, in which all elements have the same and known probability of being selected^{[1]}If this condition is not met then the selection of elements (sampling) is considered as a non-probability sampling.The non-probability sampling is a method that does not even give some elements in a sampling a chance to be in it.

The main rules of probability sampling are^{[2]}:

- knowledge of number of the whole population when a sample is selected
- knowledge of the probability of selecting each of the elements

The **"nonzero"** definition in probability sampling says that each element in the population should have a higher chance of getting into the sample - at least more than 0%. The non-probability sampling is a method that does not even give some elements in a sampling a chance to be in it. In population studies, it is very important to choose an appropriate sampling method because each has its own advantages and disadvantages. Some people try to combine the two methods together ^{[3]}.

## Advantages of probability sampling

Probability sampling is characterized by a great number of advantages due to its prevention of bias in conducting research.
Research has shown that people who conduct research on the sample and do not use probability sampling are guided by their own emotions and preferences. This way of sampling has the great advantage of being **very objective** and not guided by personal subjective preferences.
Another advantage of such an objective and random selection method is the possibility of using a **large number of mathematical research** methods. Thanks to them, it is possible to conduct tests and draw conclusions from them. By randomly selecting the elements, the assessment of such a sample is effective.
The last important advantage that characterizes probability samplin is its **universality**. The method can be used several times and repeated all the time. It can be used in any situation and at any time^{[4]}.

## Footnotes

- ↑ Heavey E., (2011),
*Statistics for Nursing*, Jones & Bartllet Learning, p.65 - ↑ Daniel J.,(2012),
*Sampling Essentials: Practical Guidelines for Making Sampling Choices*, Sage Publications ltd, p. 66 - ↑ Daniel J.,(2012),
*Sampling Essentials: Practical Guidelines for Making Sampling Choices*, Sage Publications ltd, p. 67, 75-79 - ↑ Humenik J., Hayne D., Overcash M., Gilliam J., Witherspoon A., Galler W., Howells D., (1980),
*Probability Sampling to Measure Pollution from Rural Land Runoff*, North Carolina State University Raleigh, p. 41-45

## References

- Daniel J.,(2012),
*Sampling Essentials: Practical Guidelines for Making Sampling Choices*, Sage Publications ltd, p.65-79 - Hájek J., (1959),
*Optimal strategy and other problems in probability sampling*, Časopis pro pěstování matematiky Vol. 84 No. 4, p. 387-423 - Heavey E., (2011),
*Statistics for Nursing*, Jones & Bartllet Learning, p.65-66 - Humenik J., Hayne D., Overcash M., Gilliam J., Witherspoon A., Galler W., Howells D., (1980),
*Probability Sampling to Measure Pollution from Rural Land Runoff*, North Carolina State University Raleigh, p. 41-45 - Tansey O., (2007),
*Process Tracing and Elite Interviewing: A Case for Non-probability Sampling*, Political Science and Politics Volume 40 - Thomas R.,(1985),
*Estimating Total Suspended Sediment Yield With Probability Sampling*, Water Resources Research vol.21 no. 9, p.1381-1384

**Author:** Maciej Plęskowski