The phenomenon of the F statistics is typically referred to as the value retrieved in the course of carrying out an ANOVA test (Hahs-Vaughn & Lomax, 2013). Similarly, the F-statistics-related data can be acquired when running a regression analysis. Seeing that the two tests mentioned above are aimed primarily at determining the differences between the samples of the population under analysis, it will be reasonable to assume that the concept under discussion is a tool for determining whether there is a joint significance in a group of particular variables.
Differently put, an F statistic is any random variable within the range of the F distribution. At this point, the concept of the cumulative probability (CP) must be brought up. By definition, CP is the probability of a variable that has been sampled randomly to fall within a particular range. With the definition provided above in mind, one may assume that F statistics should be related to the instance of retrieving a unique CP (Baron, 2013).
The application of the F statistics as a tool is a quite common stage of carrying out a test in an experimental setting. For example, when evaluating the effects of different types of treatment, with no specific preferences to any thereof, one may apply the randomized approach to testing the efficacy of the drugs. Thus, F Statistics should be used to locate all possible random treatments (Stommel & Dontje, 2014).
A test for the occurrence of the systolic blood pressure among the patients in the setting of a particular hospital can also be interpreted as the environment, in which the F statistics can be applied to locate the links between the variables. In case there are several patient groups (e.g., age-based, such as 20-29, 30-39, 40-49, 50-59, and 60-69 years old), as well as several competing treatment plans (e.g., Plan A, Plan B, Plan C, and Plan D), the F statistics should be used to locate possible differences between the overall group mean and the mean in the sample group chosen for the analysis.
Similarly, when determining the differences in the efficacy of applying an employee-focused and a customer-focused approach, the F statistics should be used to measure the possible differences in the performance rates of the staff, as well as the customer satisfaction levels (Wilson, Hill, & Glazer, 2013).
It should be noted, though, that the use of the F statistics as one of the analysis stages may cause a range of difficulties when retrieving let critical values in the process. Because of the complexities associated with the analysis thereof, it is typically suggested strongly that the right-tail test should be preferred to the left-tail one; thus, the analysis will be carried out in a much faster manner. The identified issues are traditionally viewed as an integral part of a two-tailed test (Groebner, Shannon, & Fry, 2014).
F statistics is usually interpreted as a stage of the ANOVA process or the regression analysis that involves the location of a possible joint significance between the variables analyzed. Despite the issues related to the identification and the further processing of the left-tailed test outcomes, the application of the F statistics is considered an essential step in determining the existence of a correlation between the variables under analysis. Therefore, the subject matter should be incorporated into a statistical test to prove the null hypothesis either right or wrong.
Baron, M. (2013). Probability and statistics for computer scientists (2nd ed.). Chicago, IL: CRC Press.
Groebner, D. F., Shannon, P. W., & Fry, P. C. (2014). Chapter 12. Analysis of variance. In Business statistics (9th ed.) (pp. 543-546). Upper Saddle River, NJ: Prentice Hall.
Hahs-Vaughn, D. L., & Lomax, R. G. (2013). An introduction to statistical concepts (3rd ed.). New York, NY: Routledge.
Stommel, M., & Dontje, K. J. (2014). Statistics for advanced practice nurses and health professionals. New York, NY: Springer.
Wilson, R., Hill, A. V., & Glazer, H. (2013). Tools and tactics for operations managers (collection). New York, NY: FT Press.