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Introduction:
Probability and non-probability sampling are fundamental concepts in research methodology. Sampling refers to the process of selecting a subset of a population for study. Probability sampling is a sampling technique in which all members of the population have an equal chance of being selected for study, whereas non-probability sampling does not guarantee this equal chance of selection. In this essay, we will discuss the characteristics of probability and nonprobability sampling. Additionally, we will explore why researchers use conditional probability in their studies.

Characteristics of Probability Sampling:
Probability sampling is characterized by the following features:
1. Randomness: The selection of participants is by chance and there is no bias towards any specific individuals or groups.
2. Representative: As each member of the population has an equal chance of being selected, the sample is likely to be representative of the population being studied.
3. Precise: Probability sampling allows researchers to calculate the sample size and thereby provides a precise estimation of the population parameters.
4. Less chance of sampling errors: Probability sampling ensures that the probability of sampling errors is minimized.
5. Allows for statistical inference: Probability sampling allows researchers to apply statistical techniques to analyze the data and generalize the findings to the population.

Characteristics of Non-Probability Sampling:
Non-probability sampling is characterized by the following features:
1. Non-randomness: The selection of participants is not by chance, but rather based on some criteria such as accessibility or researcher judgment, leading to a higher likelihood of bias.
2. Not Representative: Non-probability sampling may not represent the population being studied.
3. Less Precise: Non-probability sampling may not allow for a precise estimation of the population parameters.
4. More chance of sampling errors: Non-probability sampling could lead to higher sampling errors if the sample does not represent the population.
5. Statistical inference not possible: Non-probability sampling does not allow for statistical inference or generalizability of the findings to the population.

Conditional Probability vs Unconditional Probability:
Researchers use conditional probability when they want to examine the likelihood of an event occurring given that another event has already happened. For example, in medical research, the probability of someone having the flu given that they were exposed to someone with the flu is an example of conditional probability. In contrast, unconditional probability is the probability of an event occurring with no prior knowledge of whether any other events have happened. Researchers use conditional probability in their studies because it provides a more targeted approach to examine the relationship between variables. For example, researchers may be interested in the probability of someone developing a particular disease if they have a genetic predisposition to that disease. This would require conditional probability.

Conclusion:
Probability and non-probability sampling are essential concepts in research. Probability sampling is preferred over non-probability sampling as it allows for statistical inference and provides a more precise estimation of the population parameters. Alternatively, researchers use non-probability sampling when probability sampling is not feasible. Finally, researchers use conditional probability when examining the relationship between variables.