What is Probability Sampling with
examples?
Probability sampling:
A sampling approach in which the researcher picks samples from diverse populations using a strategy based on the notion of opportunity is known as possible sampling. A participant must be chosen at random to be considered as a representative sample of opportunities(Tillé & Wilhelm, 2017).
Probability
sampling uses statistical theory to choose a small group of people
(sample) at random from a large population and then estimate that all of their
replies will equal the total population(Tillé & Wilhelm,
2017).
The
most crucial criterion for sampling is that everyone in your society has an
equal and known chance to vote. If you had a population of 100 people, for
example, 1 out of every 100 concerns may be chosen. Possible sampling provides
you the highest chance of obtaining a sample that accurately reflects the
population(Chen, 2019).
A
probability sample may be divided into four categories.
ü Simple random sampling
ü Systematic sampling
ü Stratified sampling
ü Cluster sampling
1.
What is Simple random
sampling?
A simple random sample is a subset of a
statistical population in which each element of the subset has an equal
probability of being taken. A simple random sample is devoted to being an
unprejudiced representation of a set.
Example:
An example of a simple random sample
would be the names of 25 workers being chosen out of a cap from a company of
250 workers. In this case, the population is all 250 workers, and the sample is
random because each employee has an equal chance of being taken. (Roy, 2019)
2.
What is Systematic Sampling?
Systematic sampling is a type of probability sampling approach in which sample elements from a weighty population are taken according to a random starting point but with a set, periodic interval. This interval, called the sampling interval, is calculated by parting the population size by the asked sample size. Despite the sample population being chosen in advance, s
systematic sampling is quite permitted as existing random if the periodic interval is determined previously and the starting point is random. (Tillé & Wilhelm, 2017)
3. What is Stratified random sampling?
Stratified random sampling is an
approach of sampling that involves the separation of a population into smaller
sub-groups known as strata. In stratified random sampling, or grouping, the
strata are formed predicated on members' participated attributes or
characteristics such as income or educational achievement. (Shabbir & Gupta,
2017)
4.
What is Cluster sampling?
Cluster sampling is a probability
sampling way in which divide a population into clusters, such as districts or
schools, and then randomly select some of these clusters as your sample(Verma, 2019)
When to Use Probability Sampling?
1. To decrease sample bias: This sampling
approach is employed when bias should be kept to a minimum. The quality of the
research description is heavily influenced by sample selection. The method by
which researchers pick their sample has a substantial impact on the quality of
the researcher's conclusions. Because it gives a fair sample of the population,
possible sampling contributes to the acquisition of high quality.
2. Where population size differs: This strategy is
often used by researchers since it allows them to construct samples that fully
represent the population. It states that we aim to learn how many people prefer
medical trips to their nations. This sampling approach will aid in the
selection of samples for various socioeconomic groups, backgrounds, and so on
to reflect the population.
3. Obtaining an accurate finding: Sample
opportunities assist researchers in obtaining reliable samples of their
subjects. To get well-defined data, the researchers employed recognized
statistical approaches to generate accurate sample sizes.
References
Chen, Y. (2019). Hierarchical variable probability sampling
for carbon estimation the University of New Brunswick.].
Roy, M. (2019).
Sampling methods: A survey. In Research
Methodology for Social Sciences (pp. 181-205). Routledge India.
Shabbir, J.,
& Gupta, S. (2017). Estimation of finite population means in simple and
stratified random sampling using two auxiliary variables. Communications in Statistics-Theory and Methods, 46(20), 10135-10148.
Tillé, Y., &
Wilhelm, M. (2017). Probability sampling designs: principles for the choice of
design and balancing. Statistical Science,
176-189.
Verma,
J. (2019). Sampling Techniques. In Statistics
and Research Methods in Psychology with Excel (pp. 291-332). Springer.