Probability sampling and non-probability sampling are two common sampling techniques used in research studies. In probability sampling, each member of the population has a known and equal chance of being selected for the sample. On the other hand, in non-probability sampling, members of the population are not selected at random, and the chances of being selected are unknown. Let’s discuss the characteristics of probability and non-probability sampling in detail.

Characteristics of Probability Sampling:

1. Representativeness: Probability sampling ensures that the sample represents the population accurately. As each member has an equal chance of being selected, the sample reflects the characteristics of the population.

2. Randomness: Probability sampling is a random process that eliminates bias and ensures equal opportunities for all members of the population to be selected.

3. Generalizability: The results of a probability sample can be generalized to the population. Since the sample represents the population, the results can be applied to the larger group.

4. Precision: Probability sampling provides an estimate of the precision or margin of error of the sample data. This estimate helps researchers to determine how confident they can be in the results.

Characteristics of Non-Probability Sampling:

1. Convenience: Non-probability sampling is convenient and easy to carry out. Researchers can select participants who are readily available or accessible.

2. Flexibility: Non-probability sampling is flexible and can be adapted to fit the research objectives.

3. Cost-effective: Non-probability sampling is cost-effective and can save time and resources.

4. Focus: Non-probability sampling can be used when the research objective is specific, and the sample size can be small.

Conditional probability is the probability of an event occurring given that another event has already occurred. It is used in situations where the occurrence of one event affects the probability of the other event. Unconditional probability, on the other hand, is the probability of an event occurring without any consideration of other events. Researchers would use conditional probability in their study when they want to explore the relationship between two events or variables. For example, in medical research, conditional probability can be used to determine the likelihood of a disease given a specific symptom. In marketing research, conditional probability can be used to determine the likelihood of a customer buying a product given their demographic characteristics. In some cases, researchers may use conditional probability instead of unconditional probability because conditional probability provides more accurate and relevant information. It can help researchers to identify patterns and relationships that would not be evident with unconditional probability. To support this, consider a study conducted by Li and Liang (2021), which used conditional probability to explore the relationship between customers’ perceived value, satisfaction, and loyalty in the online shopping context. The study found that conditional probability analysis provided a more accurate representation of the relationship between the variables compared to unconditional probability analysis. Therefore, researchers should carefully consider which type of probability to use in their study based on their research objectives. 

References:

Li, X., & Liang, C. (2021). The impact of perceived value and satisfaction on loyalty in online shopping context: A conditional probability analysis. Journal of Retailing and Consumer Services, 59, 102347. https://doi.org/10.1016/j.jretconser.2020.102347