Carrying out a quantitative analysis is a crucial step toward identifying essential relationships between the variables under analysis. It should be noted, though, that the process in question can be carried out in a variety of ways, probability distribution being one of the tools that can be used for data assessment. By definition, the phenomenon of the probability distribution can be defined as the possibility for a specific variability to occur on the identified interval. The types of probability distributions, in their turn, are quite numerous, including discrete, absolutely continuous, and Gaussian ones, to name just a few.
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The opportunities that the probability distribution analysis opens beg a range of questions. For instance, the process of sampling could use a more detailed characteristic. Differently put, the formulas required to define a sample suitable for a particular estimation could be clarified in a more detailed manner (Professor Leonard, 2011).
Similarly, the concept of statistical interference may raise a range of questions, the key one concerning the scale between the sample and the population that it might refer to. As a rule, the principle of statistical inference dictates that a sample can provide information about a wider population. However, the correlation between the sample and the population that it supposedly described is not defined, hence the question (Conmy, 2013).
The links to the probability theory, which the subject matter has, allow a foray into the area of the relativity theory. As a result, the questions regarding the hypothetical modeling of the possible outcomes of the analysis emerge. Specifically, the issue regarding the precision of the analysis outcomes and the further application of the calculation results need to be brought up as the primary areas of concern.
On the one hand, the calculation outcomes should be taken with a grain of salt due to the possibility of an error in the calculations or the occurrence of an unlikely yet possible scenario. On the other hand, the probability distribution function defines the variance of a certain phenomenon’s occurrence; therefore, it should predict the emergence of the subject matter with mathematical accuracy, which it does not (Groebner, Shannon, Fry, & Smith, 2014).
Despite the answers that the concept of the probability distribution as a function has to offer to the target audience, it opens an even larger plethora of questions that need to be answered. Most of these questions concern the degree, to which the outcomes of the calculations can be viewed as credible. However, apart from the above issue, the problem regarding the scale, to which the results of the assessment are to be applied, deserves to be brought up as a legitimately interesting area to investigate (Lecture 1. Overview of some probability distributions, 2016).
Although the phenomenon of the probability distribution is rather basic and seemingly does not imply any intrinsic issues, it turns out to be rather complex, triggering a range of questions about the outcome’s credibility and the evenness of the distribution process.
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The questions mentioned above are admittedly basic and are likely to be resolved with a more detailed study of the probability distribution and its characteristics. Moreover, the isolation of some of the features thereof is likely to be more successful in the context of addressing a particular problem. However, at present, these are the opportunities that the concept under analysis has to offer is what needs to be in the focus.
Conmy, K. (2013). Statistical estimation and sampling distributions. Web.
Groebner, D. F., Shannon, P. W., Fry, P. C., & Smith, K. D. (2014). Business statistics: A decision-making approach (9th ed.). Upper Saddle River, NJ: Pearson.
Lecture 1. Overview of some probability distributions. (2016). Web.
Professor Leonard. (2011). Statistics lecture 6.4: Sampling distributions statistics. Using samples to approx. populations. Web.