Quantitative Methods Questions Medium
Probability sampling and non-probability sampling are two different approaches used in quantitative research to select a sample from a larger population. The main difference between these two methods lies in the way the sample is selected and the extent to which the sample represents the population.
Probability sampling is a method where each member of the population has a known and equal chance of being selected for the sample. This means that every individual or unit in the population has a probability of being included in the sample, and the selection process is based on randomization. Probability sampling methods include simple random sampling, stratified random sampling, systematic sampling, and cluster sampling. These methods ensure that the sample is representative of the population, allowing for generalization of the findings to the larger population.
On the other hand, non-probability sampling does not involve random selection and does not provide an equal chance for all members of the population to be included in the sample. Non-probability sampling methods are based on subjective judgment and convenience. Examples of non-probability sampling methods include purposive sampling, snowball sampling, quota sampling, and convenience sampling. Non-probability sampling methods are often used when it is difficult or impractical to obtain a random sample, or when the researcher wants to focus on specific subgroups within the population. However, the findings from non-probability samples cannot be generalized to the larger population with the same level of confidence as probability samples.
In summary, the main difference between probability and non-probability sampling lies in the random selection process and the representativeness of the sample. Probability sampling ensures that each member of the population has an equal chance of being selected, allowing for generalization of findings to the larger population. Non-probability sampling methods, on the other hand, do not involve random selection and may not provide a representative sample, limiting the generalizability of the findings.