Comment 1

Random sampling usually provides a sample that is representative of a population because each member of the population is selected independently and has an equal chance, or probability, of being included in the study. (Grove, 2015, p. 37). It is important to conduct random sampling because it allows the study to be presented in the most unbiased form of data collection. Choosing factors at random and by chance provides a general understanding for what statistic is being collected. It limits and prejudice from the collectors, whether it is intentional or unintentional selections for the study. On the contrary, there are complications that may arise in random sampling. When sampling, there must be multiple trials in order to fully grasp a complete understanding that the statistics are accurate. Collecting one sample is only a snap shot of what is occurring at that one particular time. Multiple data collection trials should take place to guarantee the accuracy based on reoccurrence and the mean and mode of the elements in question. With random sampling, your data lacks a baseline to compare information because elements are nonspecific. To prevent from having an unsteady baseline for random sampling, some limitations must be put into play to ensure accuracy of the statistic. For example if you wanted to conduct a study about how often people wash their hands through out the day, maybe conduct the study among a certain occupation or hobby instead of generalizing the population.

Comment 2

Random sampling (also known as probability sampling) is to extract samples with equal, unbiased approach to include in the study. The goal of this method is to have subset of participants or samples that will most represent the target population. This is one of the most common methods used in statistics in conducting a quantitative research to decrease sampling error. Before random selection, a sampling frame (or list of all elements in the population from which sample is drawn) is established (Grove & Cipher, 2017). With this, all participants have same probability of being chosen.

The limitations or issues in using random sampling are the time and costs required gather full list of the population. It requires great effort (man power) to contact possible participants. Another problem is the size of the sample; it might be too small to represent the target population. An alternative to random sampling is to utilize systematic sampling. Systematic sampling is a method where a participant is picked based on random starting point, every “kth” position or interval. k is obtained by dividing the potential participants by desired sample size (Grove & Cipher, 2017). For example, in a population, there are 2000 possible nurses (samples) who can participate in the survey and desired sample size is 200 nurses. So, 2000 ÷ 200 = 10, which means, the 10th nurse on the list will be the participant. With this method, researcher must ensure that the original list was pre-ordered to avoid influencing the result.

Comment 1

Concerning the different methods argued in the “Visual Learner: Statistics,” it is important to abide on the statistical hypothesis. There are two chief types of analysis. They are null hypothesis in which samples apparent result as a result of chance and alternative hypothesis where sample observation is predisposed mainly by non-random causes. There are a total of five sampling techniques that have been discussed with systematic sampling, simple random sampling, cluster sampling, convenience sampling and stratified sampling. Systematic sampling is a type of probability sampling method where the sample members coming from the larger inhabitants are designated based on a preparatory point random as well as a fixed periodic interval. 

An example includes when a researcher wants to create a systematic sample in a university of about 90,000 enrolled students, they should consider every 100th student. A simple random sample denotes a set of data where every person has an equivalent probability of being chosen. An example includes selecting 50 members from a set of 400 employees to represent a company in an individual event. Cluster sampling is a sampling method where the population is divided into groups referred to as clusters.

An example would include getting the level of educational performance of high schools in the entire nation. A convenience sample can be categorized among the non-probability sampling methods. It is made up of people who can be reached speedily like business operators in a center and family members. An example includes an interview by a pollster to a purchaser who functions at a local mall. Finally, Stratified sampling refers to a method of sampling where the population effects to be distributed into minor groups that are called strata. The layers are therefore, formed basing the arguments on the shared characteristics and attributes of the members.

Comment 2

The visual learner statistics page includes five different samplings which include cluster sampling, simple random sampling, systematic sampling, convenience sampling, and stratified sampling.

Cluster sampling is dividing the population into sections. The cluster sampling is chosen randomly and it includes all the individuals of that particular cluster that was chosen. An
example where cluster sampling could be used is at area high schools. Many towns have several high schools within a descent range where it could be easy to pick a grade and randomly select a high school.

Simple random sampling allows every member of the study population to have an equal opportunity to be chosen as a study subject. Random sampling is known to eliminate bias by giving the population an equal chance to be chosen. An example where simple random sampling could be at a hospital and involve all register nurses.

Systematic sampling is choosing a larger population and selecting the individuals at a random starting point and a fixed periodic interval, for example picking the every fifth person (Business Dictionary, 2018).  Systematic sampling could be used in a large city of businesses and every fourth street in the district could be selected.

Convenience sampling is just that the convenience of selecting the individuals. Convenience sampling be simply your closet friends or coworkers.

Stratified sampling is choosing a general population that share the same qualities and features and separated them into different groups. An example of using stratified sampling is choosing a group of men and a group of women and dividing them into different groups.