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Q1 Sampling is the process of identifying a portion of a relevant population and studying that portion to determine inferences about the whole population

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Q1

Sampling is the process of identifying a portion of a relevant population and studying that portion to determine inferences about the whole population. Sampling is used when it is too difficult or time consuming to survey the whole population. For example, sampling can be used to determine average household size or age groups in a city. It might be impossible to survey everyone in the county, so a random portion can be identified instead. It is important to define who makes up the relevant population, called the frame, so the sample size can be chosen from that specific population, called the sampling units. It is also important that members of the sampling unit are chosen at random to avoid bias and inaccuracy. For example, selecting a sample size from one only one neighborhood can produce inaccurate results about the average household size in a city as a whole.      

The common sampling methods are simple random, systematic, stratified, cluster, and multistage sampling. A simple random sample of size n has the property that any sample of size n has the same probability of being chosen. Simple random samples are easy to understand, but not the most reliable in real world scenarios because it can result in poor representation of a large population. Systematic sampling is similar to simple random sampling, except the sample is selected according to a fixed periodic interval. Systematic sampling is sometimes preferred for its convenience over simple random sampling, but can also lead to an unrepresentative sample depending on the type of population. Stratified sampling is the process of dividing a population into defined subsets called strata and selecting random samples from each stratum. Stratified sampling can provide estimates with greater accuracy if the population parameters within each strata are well defined and with little variability. Cluster sampling is the process of dividing a population into clusters and then using simple random sampling to select a cluster.

I think stratified sampling is the most appropriate method to use, unless a more convenient method will produce results with similar accuracy. For example, if a business wants to study the purchasing habits of its customers, stratified sampling would best represent its population. The whole population can be divided into strata based on specific characteristics like age, income level, or geographic location. Selecting a sample from each stratum will then produce results with greater accuracy than randomly selecting a sample that might not effectively represent the group as a whole.

 

Q2

ways to measure sampling units which are through frames (Albright and Winston, 2017). Through the use of sampling, we can make better judgments or decisions of the information that is provided by the overall population. One particular thing is that the process can be very time-consuming and costly; therefore, we must use the correct sampling method when sampling.

               There are multiple different ways to conduct sampling. Some sampling methods are random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling (Albright and Winston, 2017). Random sampling is conducted to avoid any biases. It is naturally normal to have biases towards a particular age group, gender, etc. However, with random sampling, we can eliminate biases by sampling a population that has no set pattern. Systematic sampling is a method that gathers a wide range of population that has a slim chance of being selected multiple times(Albright and Winston, 2017). Stratified sampling is the selection of smaller groups from a population (Albright and Winston, 2017). With stratified sampling, one can select a subgroup and sample it to gather more information on its performance or commonalities. Cluster sampling is used to obtain specific data from a geographical group (Albright and Winston, 2017). Clusters can be populations that are broken into different subdivisions (Albright and Winston, 2017). Multistage sampling is conducted on a larger scale, and it can be done by doing surveys across many populations (Albright and Winston, 2017).

               Realistically, the most appropriate sampling method is stratified sampling which provides more accurate data (Albright and Winston, 2017). Stratified sampling in my job as an educator can provide information on how each student performs on evaluations. However, if we wanted to sample the different age groups of our students and compare that information to their performance in the class, we will then have a better understanding of their performance based on experience and age group. By simply breaking the information down into smaller populations we can eliminate missing any pertinent or relevant data that may be a factor in identifying deficiencies or strengths.