Answer

Differences between Explanatory and Confirmatory Analysis

Big data refers to extremely large sets of data produced by various forms of technology. Big data is studied by data analysts to establish a specialized data processing software. The data sets range from statistics of a professional sports team to political mailing lists. A data analysts is employed in revealing trends, associations, and patterns, especially in relation to human interactions and behaviors (Neumann, Kosson, & Salekin, 2017). Data analysis can either be exploratory or confirmatory. This paper seeks to highlight the differences between confirmatory and explanatory data analysis.

Often, exploratory analysis is regarded as a philosophy. There are several ways of approaching it. The procedure requires one to figure out what to make of the information, examine the questions to be asked and the strategy of framing them (Jebb, Parrigon, & Woo, 2017). Thereafter, one must identify the best way to present and manipulate the data to draw out important insights. Exploratory analysis allows data analysts to allocate clues and trends that can help to generate a conclusion.

Contrarily, confirmatory analysis as a process operates backwards. It weighs the merits of the results of data analysis. It is more of examining evidence while questioning witnesses through a trial. It focuses on processes like testing hypotheses, production of estimates, examining the relationships between variables and evaluating differences between planned and the actual outcome.

In conclusion, exploratory analysis is a method used to explore big data sets that produce predictions and conclusions. Contrarily, confirmatory analysis is a process used to evaluate evidence by critiquing the assumptions about the data.