Population is all elements in a group. For example, college students in a city are a population that includes all of the college students in that city. 25-year-old people in a country is a population that includes all of the people that fits the description. It does not make sense to do analysis on population because it is resource intensive. Therefore, we use samples. Sample is a subset of a population. For example, “1000 college students in a city” is a subset of “Total college students in a city” population. The statistical constants of the population, e.g., mean, variance etc. are referred to as parameters whereas statistical measures computed from the sample observations alone e.g., mean, variance etc. are referred to as statistics. The statistic which may be regarded as an estimate of parameter, obtained from the sample, is a function of the sample values only.
A finite subset of statistical observations (individuals) in a population is called a sample and number of observations in that sample is referred to as the sample size. Sampling methods can be of two types- probabilistic and non-probabilistic.
Probabilistic Sampling Methods
A. Simple random sampling
In simple random sampling, each sample has an equal chance of getting selected. The right way to do random sampling without any bias is by assigning Tippet’s random number against each unit and then these assigned series is thoroughly shuffled and then the random numbers are chosen one by one. Simple random sampling reduces selection bias. A disadvantage of simple random sampling is that we may not select samples based on any particular characteristic of interest, especially if that characteristic is uncommon.
B. Systematic sampling
In Systematic sampling, samples are chosen at regular intervals. the process ensures that an adequate sampling size is maintained. Systematic sampling is often more convenient than simple random sampling but it may also lead to bias if the way the samples are chosen coincide with the interval pattern.
C. Stratified sampling
In stratified sampling, the target population is divided into subgroups (or strata) based on similar characteristics. This type of sampling is used when we expect the characteristic of interest to vary between the different subgroups and we want representation from all the subgroups. Stratified sampling improves the accuracy and representativeness of the results by reducing sampling bias.
D. Clustered sampling
In a clustered sample, the population is divided into subgroups, known as clusters. These clusters are randomly selected for the study. However, these clusters are usually already defined, for example, cities or towns can be clusters. Cluster sampling can be more efficient than simple random sampling, especially when a study takes place over a wide geographical region. This technique can be disadvantageous when the selected clusters are not representative of the population which in turn will increase the sampling bias.
Non-Probabilistic Sampling Methods
A. Convenience sampling
In Convenience sampling participants are selected based on availability and willingness to take part. This technique can be useful. However, the results can be biased too as those who volunteer to take part may be different from those who choose not to. We need to note that volunteer bias can be a risk of all non-probability based sampling methods.
B. Quota sampling
Quota sampling is often used by market researchers. For example, an interviewer might be told to select samples based on a required quota for a research e.g., 20 men who are more than 25 years old or 30 teenage girls for asking about their opinion before launching a particular product in the market. This technique is pretty straightforward and representative of target population. However, the chosen sample still may have voluntary bias as mentioned earlier.
C. Purposive Sampling
Purposive sampling relies on the judgement of the researcher while choosing participants. Researchers choose a “representative” sample to suit their needs. Purposive sampling is time-and cost-effective, however, in addition to volunteer bias, it is also prone to errors of judgement by the researcher in choosing participants.
Conclusion
Sampling is the cornerstone of modern statistical analysis, allowing researchers to draw meaningful insights from large populations without the need for exhaustive data collection. While probabilistic sampling methods offer greater reliability and representativeness by minimizing bias, non-probability methods provide flexibility and efficiency, especially in real-world constraints. Choosing the right sampling technique depends on the research objectives, available resources, and the level of precision required. Ultimately, a well-designed sampling approach ensures that conclusions drawn from a sample can be confidently extended to the broader population, laying the foundation for sound, data-driven decision-making.