"A" WORK PLEASE

Power is the probability to detect an effect that is present in an event under consideration. It is also the capability of correctly rejecting the null hypothesis so as not to conclude that the results were not significant when instead they were. The elements of power are: significance level of alpha, sample size and effect size. In conducting research, a researcher should be able to set a minimum power for the study of 80% which is considered to be sufficient for research (Ramey, 2005).

The research in this case study had a limited number of participants to support the study. A limited number of participants will not be able to obtain adequate information for the research study. If a large power size is used in a research, it requires a large sample size to provide information that will aid in making a decision that is not biased on either of the variables. In this case study, the much needed information was not available because the research did not incorporate sufficient number of participants. The limited number of participants will not aid the performance of a two-tailed test as this test requires a large sample size as compared to one tailed test (McLaren, 2017).

In order to obtain adequate information, a considerable number of participants have to be engaged. Engaging these participants would require more financial resources to perform the research activity. The participants will require to be facilitated financially so as to perform the assignment given. Researcher will be obliged to take care of all logistics requirements to the participants to ensure that the research objective is achieved. If a researcher does not get financial support from partners or even well-wishers, then this proves to be a problem to accommodating a large sample size (McLaren, 2017).

The research article used a power estimate of 80% being the minimum power of any study. This power is sufficient to help the researcher to detect differences that exist in a population. However, the study sample size should be large. The research provided a significance level of 0.05 or 0.01 (McLaren, 2017). These levels are not much stringent and they will therefore require smaller sample sizes in order to detect any significant differences that may be prevalent in the variables.

This population is estimated to be the right size because of the level of significance that is set at 0.05 or 0.01. This level will require a fairly small sample size in establishing differences in the study variables. Considering that power is set at 80%, this provides the capacity to rejecting the null hypothesis correctly.

Size of	Sample Size (n)
Population
10	2
15	4
20	6
25	9

Table 1: Too few number of participants

Size of	Sample Size (n)
Population
100	16
150	18
200	22
250	29

Table 2: Ideal number of participants

Size of	Sample Size (n):
Population
1000	168
1500	175
2000	191
2500	198

Table 3: Too many number of participants

Table 1 has a population that is small. A small population will not be able to provide the differences that may exist in the variables under examination. However this small population will be cost effective since no much financial resources will be needed in the obtaining information.

Table 2 has data that is considered to be having the ideal number of participants. The population is neither small nor large, it is moderate. This population will be able to provide significant difference on the variables in the research study. In terms of cost, this sample size is fairly off compared to one that may be having a large population of too many participants.

Table 3 represents data for too many numbers of participants. The data is voluminous for examination purposes. Although this data will be suitable for significance levels that are more stringent since they require a large sample sizes. In relation to costs, this large number of participants will require a lot of financial resources to support them during the research activity.

References:

McLaren, C. E. (2017). Sample size and power determination when limited preliminary information is available. BMC Medical Research Methodology, 174(1), 75.

Ramey, S. L. (2005). Assessment of health perception, spirituality and prevalence of cardiovascular disease risk factors within a private college cohort. Pediatric nursing, 31(3), 222.