Analyzing Descriptive Statistics:
What statistical procedure is needed to determine an effective sample size to make a reasonable conclusion?
Main objective of having or developing an effective sample size is trying to get both clinically and statistical significant result as this also tries to maximize the use of the resources efficiently. A determinant of sample size may include the size in question, homogeneity of the sample the anticipated attrition, and sometimes the risk of errors which have been considered as being appropriate depending on the research question being studied. In determining the sample size of the given sample, one would have to accurately define the outcome which is the objective being measured. Another determinant is the homogeneity of the sample which shows the similarity of the study units and the best way that this sample would be representing the entire population (Burmeister and Aitken, 2017).
At some point, this also includes determining the different parameters including the standard deviation. It’s important to note the essence of level of significance as this helps in identifying whether some effect may be existing. Power of a test is also important as this includes the probability of not detecting the effect in the event when there is an effect. In that note, I may argue that the sample size determination procedure may include pinpointing the outcome or a given hypothesis which in this case it includes a null and alternative hypothesis, then one selects the smallest effective size then one may specify the significance of the test, estimate the different parameter values that may be required in calculating the sample size and specifying the intended power. In some cases, the sample size may also be dependent on the design and the different parameters that are being measured.
Reading through the study, you observe that the researcher used a chi-square analysis to analyze nominal and ordinal data. Is this the appropriate level of statistical analysis to answer the research question? Explain your rationale.
Chi square analysis is the efficient statistical test that can be incorporated in answering different research questions. From the different study readings, we may denote that chi-square analysis is one of the many statistical tests that may be useful in describing the data as this statistical test does not provide the casual relationship between these two variables. The chi-square analysis may be incorporated in describing and outlining he differences in the nominal and the ordinal variables. Also, it’s worthwhile noting that the study description may also show that the given data was not normally distributed. In that note, we may conclude that chi-square may also be used in the data which may not be depending on normal distribution in interpreting the findings of the study. This makes it a non-parametric test that can be used in checking association among the different categorical variables (McHugh, 2018).
A chi square is essential, just as stated, its can be used in analyzing nominal and categorical data as when the chi-square helps in analyzing both the ordinal and the nominal data, the different statistical tests are incorporated inn finding different problems by approaching the problems of ordinal variables. For instance the Mann-Whitney test is appropriate in evaluating the differences that exists between different populations that are using a given data from an independent measure design. On the other hand, other tests also tries in evaluating the differences between different populations by incorporating the different samples for a given treatment condition.
Reading further, the researcher reports that the p-level led her to conclude that the null hypothesis was rejected. In your critique of the study, you determine that the null hypothesis is true. Do these findings impact your decision about whether to use this evidence to inform practice change? Why or why not?
From the critique, the said findings would have an effect on the decision as I would have to evaluate whether I can incorporate this evidence in informing some practice change. The reason for this is because the researcher committed type 1 error which is likely to lead to inefficient change that may help in adopting newer things or even may lead to erroneous results as there are constant errors in the practice. In trying to avoid such and related errors and sort of mistakes, in my case, I would try controlling for type 1 error as this would help me in correcting the errors that are likely to result to making wrong conclusions and adopt the best change practice that would help in attaining the stipulated results (Weiss and Weiss, 2017).
Statistical errors may legitimate data as this at some point may lead to getting incorrect conclusion. In the study, it’s always crucial to determine if the given results are correct because that what matters most considering the objective of the study. In the event when this results are incorrect, then it would not be necessary to interpret the data any further. Which as stated the fact that when I discover that the research made type 1 error, it would be very hard for me to make any further conclusions on the study. We have different statistics that helps us in getting evidence which helps in determining if there is any relationship that exists between some parameters. This statistics play an important role in every aspect and it’s therefore important to always take it into consideration as this may lead to different series of events of implications irrespective of where a given study was conducted.
McHugh, M. L. (2018). The chi-square test of independence. Biochemia medica: Biochemia medica, 23(2), 143-149.
Weiss, N. A., & Weiss, C. A. (2017). Introductory statistics. London: Pearson Education.
Burmeister, E., & Aitken, L. M. (2017). Sample size: How many is enough? Australian Critical Care, 25(4), 271–274. doi:10.1016/j.aucc.2012.07.002
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