Correlation Between Sampling, Sampling Risk And Nonsampling Risk

When conducting research data is gathered from a sample. The data can prove or disprove the hypothesis. When reviewing the data, a person can become bias and only use the data that they feel is beneficial to their study. Rubin and Babbie (2014) write about the two types of sampling bias: Conscious and Unconscious. The authors state “When we speak of bias in connection with sampling, this simply means those selected are not typical or representative of the larger populations from which they have been chosen” (Rubin & Babbie, 2014).

The population sampled due to its specific nature i.e., college students, and college graduates would need to be contemplated in regards to the testing results as it is offered as a depiction of the general population.

this study is the use of convenience sampling, as previously mentioned. Black et al. (2000) make

According to Schutt (2008), sampling is defined as a subset of population used in a study to be a representation of the population as a whole. My final project is a pre-hire assessment which analyzes potential risky pattern behaviors and emotions in the work place. One of the most important considerations related to sampling that will need to be addressed in my final project is defining the population that will be taking the assessment.

Explain the importance of random sampling. What problems/limitations could prevent a truly random sampling and how can they be prevented?

A population is the entire group to be studied and a sample is a portion of the population.

The difference between the mean of replicate test results of a sample and the ("true") value of the target population from which the sample was

This sample may have a bias resulting from subjects that have a special interest in the subject being studied.

a) Based on a randomly selected group of 500 patients with high cholesterol, it was found that 67% have heart disease. Is this a population or a sample; explain your answer. Raw data is collected from a subset of patient with high cholesterol to determine numbers describing characteristics of the subset (Bennett, Briggis, & Triola, 2009). The raw data collected from the 500 patients is consolidated and summarized to form sample statistics. The raw data and sample statistics are indications that this is a sample (Bennett, Briggis, & Triola, 2009).

This sampling would require that the sampling is relevant to the topic that we are trying to research and that there is an identifiable distribution among the population being interviewed. This would be required if you were to sample males versus females, one would have to be sure to determine the exact distribution level. It would be important to ensure that it was calculated that there were 45 males and 50 females and that information would have to be evaluated based on those measures rather than an equal amount of each. This could also be said if one were to interview different religious groups. If you were going to interview several different religious groups, it would be imperative to ensure that you have allowed all the numbers of the groupings that you have done.

The book simple statistics explains “Humans make mistakes inputting data, Data entry”(Meithei pg. 35), the idea that the statistic can be wrong can potentially cause confusion for the people. When people view statistics it is easy for them too believe the article with out doing any prior research this is do because all the information is present. The only way to deter individuals from believing samples is explaining the samples in a “complex to simple method”(Meithei, pg. 34) which can potentially helped the consumer understand. In the book simple statistics explains “Non problabiby sampling” (Meithei, pg. 41) means that selectin a sample where selection is unknown unregistered and impossible to estimate sampling error. An example of researcher sampling and testing being taken out of context is President Bush tax cuts. The graphs showcases Presidents bushes tax cuts “Bush’s taxes cuts being superior than the opponents”(Gregory, 2012) the graph in truth shows that Bush’s tax cuts will not benefit the country. The statistical consumer must understand that there are many graphs taking out context and is up too the consumer too research the data for correct

-The sampling distribution is a distribution of the results if all possible samples are selected, where as a population distribution is the distribution of a particular variable of interest.

According to Hair et al. (2003), in the research, the sampling process enables identifying, developing and understanding an interested object that need to be determined (p.333). Hence, in order for the researcher to carry out the sampling appropriately, advantages and disadvantages of the various sampling methods should be considered along with the theoretical component of the study (Hair et al. 2003, p. 368 f). Theoretically, the sampling procedure is divided into two major types which consist of probability and nonprobability sampling. In probability sampling, individuals have a known chance of being selected. While, in non-probability sampling, individuals do not have a known possibility to be selected (Sekaran 2003, p. 269 f). Also, the different sampling methods provide different advantages and disadvantages. Hence, the researcher should consider this point before choosing the sampling method for the

views of the problem being studied. In making a purposive sampling decision for the respondents, the goal was

For any research with large size of population, the collection and analysis of entire data is sometimes impossible due to time constraint. Alternatively, the features of population can be presented by typical sample. According to Saunders, Lewis and Thornhill (2012), researchers can make use of