Unit 6 journal dana






Unit 6 Journal Dana

Level of confidence is a term used to show the percentage of possibility that the mean, proportion or any statistics lie in that range. It increases the confidence of a person who carries out other tests. Level of confidence makes it possible for the person to predict the results. Confidence level can also be defined as an expression of showing how a researcher has confidence in the results from a sample (Webb, 2005). A system used in matches will show consistency and accuracy in results. Values of the scores are expected to gradually show a smaller range as the number of matches increases. A researcher’s level of confidence will be determined by number of factors. They are size of sample, the frequency of respondents and size of population. There was an agreement among researchers that these factors must be considered when carrying out the study (Amabile, 1989).

A confidence interval is the approximation of a range of figures, which are likely to be part of a population that is not known. The range approximated is acquired from a given data. If samples are collected severally from one population, and calculation of interval confidence is done, it is expected that the percentage of those intervals will be inclusive of the population that is not known. The width of the interval of confidence shows roughly how unsure it is about the unknown population. When calculating the confidence interval it is not necessarily compulsory to get a result of ninety five percent. Other accepted figures can be ninety, ninety-nine or even ninety-nine percent. Confidence levels are more details than just carrying out hypotheses tests. When calculating the confidence interval, selecting confidence level is what determines whether the confidence level will fall in value with the real parameter (Burdick & Montgomery, 2005).

The relationship between these two features is that they are both used in the research. They are specifically useful in the interpretation and analysis of the data collected. The confidence level is used to determine the confidence interval. This means that wrong choice of confidence level will lead to a wrong value of confidence interval. Both features work with a range of values. Confidence level is a range of values where the accurate figure is likely to be. The same case applies to the confidence interval. It also deals with a range of figures where which the approximation is being made (Leonard & VanPool, 2011). Some of the differences found between these two features are the different way of calculating. The two have different formulas because they determine different phenomena. The confidence level calculates how sure a researcher is that accuracy lies from the range of values he or she gets. The confidence interval determines the approximation of the expected results from the research. Another difference is the confidence level only uses ninety and ninety-nine percentage levels. In the confidence interval, ninety-five percent is most common, but other percentages are also acceptable (Gibbons & Coleman, 2001).

The 95% confidence interval can be any value like 27 or 30. To say it in a simpler way, one is 95% confident that the real average of a given sample data is between 27 and 30.

The 99% confidence interval may be any figure like 25 or 32. This means that a person is 99% confident that the real average of a sample group is between 25 and 32. The confidence level is directly proportional to the confidence interval (Lavrakas & Sage, 2008). When the confidence level becomes higher, it influences the confidence interval to be wider. When the level of confidence becomes lower, the confidence interval will become narrower. In normal circumstances, it would be preferred to land directly on the accurate figure. Alternatively, a researcher would like to have a narrower confidence interval and at the same time, be as confident as possible. This is why it is important to have as large a sample size as possible. This way, possible errors will be minimized as well as get a small interval (Gibbons & Coleman, 2001).



Amabile, T., Consortium for Mathematics and Its Applications (U.S.), Chedd-Angier Production Company., American Statistical Association., American Society for Quality Control., Annenberg/CPB Collection., & Intellimation, Inc. (1989). Confidence intervals.Washington, D.C.: Annenberg/CPB Collection.

Burdick, R. K., Borror, C. M., & Montgomery, D. C. (2005). Design and analysis of gauge R&R studies: Making decisions with confidence intervals in random and mixed ANOVA models. Philadelphia, Pa: Society for Industrial Applied Mathematics.

Gibbons, R. D., & Coleman, D. E. (2001). Statistical methods for detection and quantification of environmental contamination.New York: Wiley

Leonard, R. D., & VanPool, Todd L. (2011). Quantitative Analysis in Archaeology. John Wiley & Sons

Leonard, R. D., & VanPool, Todd L. (2011). Quantitative Analysis in Archaeology. John Wiley & Sons.

Lavrakas, P. J., & Sage Publications, inc. (2008). Encyclopedia of survey research methods.Thousand Oaks, Calif: SAGE Publications.

Webb, D. W., & U.S. Army Research Laboratory. (2005). Interval estimates for probabilities of non-perforation derived from a generalized pivotal quantity.Aberdeen Proving Ground, MD: Army Research Laboratory.

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