In statistics, sampling is the process of selecting a subset of data from a larger dataset. There are two main types of sampling: probability sampling and non-probability sampling. The main difference between the two types of sampling is how the sample is selected from the population.
Probability sampling is a type of sampling where every member of the population has a known and equal chance of being selected for the sample. This means that the sample is chosen randomly from the population, using a random number generator or other methods to ensure that each member of the population has an equal chance of being included in the sample. Probability sampling is considered the most reliable and unbiased method because it ensures that the sample is representative of the population and reduces the potential for bias.
Non-probability sampling is a type in which the sample members are not randomly selected from the population. In non-probability sampling, the sample may be selected based on convenience, availability, or other factors rather than random selection. Non-probability sampling is generally considered less reliable and less unbiased than probability sampling because it is not guaranteed to be representative of the population.
The main difference between probability and non-probability sampling is how the sample is selected from the population. Probability sampling is based on random selection, while non-probability sampling is based on non-random criteria. Probability sampling is considered more reliable and unbiased, while non-probability sampling is deemed less reliable and less fair.
Types of Probability Sampling
Several different sampling methods can be used for probability sampling. Some common sampling methods for probability sampling include:
- Simple random sampling: This is the most basic method of probability sampling, where every member of the population has an equal chance of being selected for the sample. This is typically done using a random number generator, lottery or other methods to select a subset of the population randomly.
- Systematic random sampling: This is a probability sampling method where the sample is selected using a systematic, random approach. This method involves selecting a random starting point in the population and then selecting every kth member of the population (for example, checking every 6th piece produced by the machine) to be included in the sample. For instance, if the population contains 1000 members, and the sample size is 100, the researcher could select a random number between 1 and 10 and then select every 10th member of the population starting from that point. This method ensures that every member of the population has an equal chance of being selected for the sample and can be more efficient than simple random sampling.
- Stratified sampling: This method involves dividing the population into different subgroups, or strata, based on certain characteristics and then randomly selecting a sample from each stratum. This ensures that the sample is representative of the various subgroups in the population. For example, if ten people are drawn to represent a country, five of them are male and five are female, to avoid gender bias.
- Cluster sampling: This method involves dividing the population into different clusters and then randomly selecting a sample of clusters. All members of the selected clusters are then included in the sample. This method is often used when it is impractical to sample the entire population and can be more efficient than simple random sampling. Sampling is often clustered by geography or by time periods. For example, survey all customers visiting particular stores on particular days.
Some common sampling methods for non-probability sampling include:
- Convenience sampling: This method involves selecting a sample based on convenience or availability. For example, a researcher may choose a sample of participants from a nearby community or a convenient location rather than selecting a random sample from the population. It's usually done when you want to get as many responses as possible quickly. Convenience samples are typically biased toward those who agree with your research question. For example, surveying friends and family members.
- Judgement sampling is a method of non-probability sampling where the sample is selected based on the judgement or expertise of the researcher. In judgement sampling, the researcher uses their knowledge and experience to select a sample that is believed to represent the population. This method is often used when it is difficult or impossible to sample the population randomly or when the researcher has expertise in a particular field that allows them to select a representative sample. However, judgment sampling is generally considered less reliable and less unbiased than probability sampling because it is not guaranteed to be representative of the population. For example, an auditor selects a sample based on their concerns in the earlier audit.
- Quota sampling: This method involves setting quotas for different subgroups in the population and selecting a sample to meet those quotas. Typically the researcher identifies the target group and then randomly selects a percentage of the group. For example, a university wants to survey student attitudes toward their courses. They know that there are around 1000 students enrolled each year, so they randomly select 200 students each year.
- Snowball sampling: This method involves starting with a small group of participants and then asking them to refer other participants who fit the criteria for the study. This process is repeated until the desired sample size is reached. This method is often used when it is difficult to access the target population.
Overall, the choice of sampling method depends on the study's specific goals and the population's nature. Probability sampling methods are generally considered more reliable and unbiased, while non-probability sampling methods are quicker and easier to conduct.