?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.
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Introduction: Sampling is an essential technique used by researchers to select samples from a population to conduct their study. The two main types of sampling techniques are probability and nonprobability sampling. In this answer, we will discuss the characteristics of probability and nonprobability sampling and why researchers use conditional probability instead of unconditional probability in their study.
Characteristics of Probability and Nonprobability Sampling:
Probability Sampling: Probability sampling is a technique used when the researcher needs to select a sample from a population, where each member of the population has an equal chance of being included in the study. The characteristics of probability sampling are as follows:
1. Random Selection: The sample is selected randomly from the population.
2. Sample Representation: The sample is representative of the population.
3. Accuracy: The sample is more accurate and reliable.
Nonprobability Sampling: Nonprobability sampling is a technique used to select a sample from a population, where not every member of the population has an equal chance of being included in the study. The characteristics of nonprobability sampling are as follows:
1. Non-random Selection: The sample is selected based on subjective criteria.
2. Sample Not Representation: The sample may not be representative of the population.
3. Less Accurate: The sample may not be as accurate or reliable.
Why Researchers Use Conditional Probability Instead of Unconditional Probability in Their Study:
Researchers use conditional probability instead of unconditional probability in their study because conditional probability takes into account the prior information when calculating the probability of an event. For example, if a person has a higher chance of having a disease based on their age, gender, and family history, then the probability of them having the disease is higher when using conditional probability. On the other hand, unconditional probability does not take into account prior information and is not as accurate in predicting the probability of an event. Therefore, researchers use conditional probability when they have prior information and want to make a more accurate prediction.
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