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**Introduction**

A sample that accurately represents a population is chosen through sampling. This is important as it makes research possible, economical, and accurate. Sampling is simply choosing a sample from a population under research. Among the factors that make the research generalizable is the sampling method. Sampling methods are classified as probability or non-probability.

**Non-probability Sampling**

Non-probability sampling employs methods that are not randomized, with the researcher mostly relying on their own judgment to select participants for the sample. Participants are selected based on ease of access, such as selecting friends or classmates. Although this approach may offer convenience and usefulness, its ability to apply findings beyond the specific sample is limited. Non-probability sampling methods such as Convenience Sampling, Purposive Sampling, Quota Sampling, and Snowball Sampling are often more cost-effective, less complex, and simpler to implement compared to probability sampling (Showkat & Parveen, 2017). These non-probability sampling techniques can be beneficial in studying particular phenomena and generating useful insights.

**Probability Sampling**

Probability sampling guarantees that each sample has an equal chance of being chosen, ensuring that every element in the population has a recognized non-zero possibility of being included. This method is beneficial in producing representative samples of the population. To illustrate, if there are 250 students in a college, probability sampling would entail randomly selecting participants from the entire student population. Suitable probability sampling techniques for this situation consist of Simple Random Sampling, Stratified Random Sampling, Systematic Random Sampling, Cluster Sampling, and Multi-stage Systematic Sampling (Showkat & Parveen, 2017).

Researchers can use conditional probability of non-conditional probability. Conditional probability is employed when two events have a relationship, and the likelihood of one event occurring is dependent on the occurrence of the other event (Hájek, 2011). On the other hand, unconditional probability is utilized when there is no relationship between the events, and the probability of one event happening is not impacted by the occurrence or non-occurrence of the other event.

**Conclusion**

Obtaining noteworthy research findings through sampling is possible. On the other hand, it is critical to acknowledge the potential disparities between the population and the sample, which can lead to errors. Thus, utilizing an appropriate and effective sampling technique is crucial.

**References**

Hájek, A. (2011). Conditional Probability. *Journal of Phylosophy of Statistics *, DOI:10.1016/B978-0-444-51862-0.50003-4.

Showkat, N., & Parveen, H. (2017). Non-Probability and Probability Sampling.

this is was qousation

This week covers probability and non-probability sampling. Discuss in detail the characteristics of probability and nonprobability sampling. Discuss why researchers would use conditional probability instead of unconditional probability in their study.

**Expert Solution Preview**

Introduction:

Sampling is a crucial aspect of research as it ensures that the findings are representative of the population under study. Sampling methods are either probability or non-probability-based. In this response, I will discuss the characteristics of probability and non-probability sampling and explain why researchers use conditional probability in their studies.

Characteristics of Probability Sampling:

Probability sampling is a random sampling technique that guarantees that each element in the population has an equal chance of being chosen for the sample. This method ensures that the sample is representative of the population under study, minimizing errors and increasing accuracy. There are various probability sampling techniques, including simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, and multi-stage systematic sampling.

Characteristics of Non-Probability Sampling:

Non-probability sampling does not involve randomization but rather relies on the researcher’s judgment to select participants for the sample. Non-probability sampling methods are often more cost-effective, less complex, and simpler to implement compared to probability sampling. However, the ability to apply findings beyond the specific sample is limited. Common non-probability sampling methods include convenience sampling, purposive sampling, quota sampling, and snowball sampling.

Conditional and Unconditional Probability:

Researchers use conditional probability when two events have a relationship, and the likelihood of one event occurring is dependent on the occurrence of the other event. This method is useful when studying cause-effect relationships between variables. On the other hand, when there is no relationship between the events being studied, researchers use unconditional probability.

Conclusion:

In conclusion, the appropriate and effective sampling technique is crucial to ensure that the findings of the research are representative of the population under study. While probability sampling techniques guarantee an equal chance of selection for each element of the population, non-probability sampling methods are more cost-effective and simpler to implement. Conditional probability is employed when there is a relationship between variables, while unconditional probability is utilized when there is no correlation between the events being studied.

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