The method of data analysis employed depends on various factors. It is evident that the analysis of econometric data varies from that applied on information derived from financial research. Moreover, qualitative and quantitative data require different analysis techniques. The same case applies to continuous and discrete data. The paper delves into the application of parametric test and their demerits and advantages. It also focuses on the implementation of earned value analysis and critical chain scheduling together with their merits and demerits. The analysis discussed below has their relevance which enables analysts to apply them depending on the circumstances.
Statistics deals with analysis aspects in the population and the sample. Consequently, it delves into the parameters and the statistic of the data (Stephanie, 2018). Thus, a parametric test investigates the population parameters regarding the origin of the data. It does not apply to data which fails to follow a normal distribution. It also lacks importance if the groups do not have a uniform variance. However, researchers can use it given a non-normal data follows the central limit theorem. The data also needs to possess a high statistical power (Stephanie, 2018). Examples of these tests include the T-test, ANOVA and product correlation coefficient.
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