A Comparison Between Descriptive and Inferential Statistics

Descriptive and Inferential Statistics
Part 1
In basic terms, descriptive statistics could be conceptualized in terms of “the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data” (McGregor, 2017, p. 112). Thus descriptive statistics are interested in describing certain features of a set of data or population. This could be differentiated from inferential statistics which, as will be described elsewhere in this text, concerns itself with inferring features of a set of data or population. We could make use of descriptive statistics to measure performance. A good example of the utilization of descriptive statistics to measure performance would be in the determination of the average performance of a specific class, such as this one, in a specific test. It is also important to note that with descriptive statistics, we can also do a comparison of different things. To give one specific example, Pyrczak (2016) points out that “we can use a descriptive statistic like the mean (or average) to compare the height of the Boston Celtics to that of the Los Angeles Lakers” (122). It is clear that in this example, the approach would come in handy in comparing different groups.
It should, however, be noted that as McGregor (2017) points out, in some instances one could be interested in more than just the simplistic description of a certain data set. This is more so the case given that descriptive statistics do not permit the formulation of conclusions beyond the analyzed data. This is where inferential statistics come in. Thus, in the words of the author, “inferential statistics allows us to draw conclusions about an entire population using only a smaller subset of the population” (McGregor, 2017, p.135). To give a better illustration of this, we could use the recently concluded elections as an example of a scenario whereby inferential statistics would be of great relevance. For instance, in the said elections, one could have drawn conclusions about the voting behaviors of people by making a determination as to how a few voters will vote. It therefore follows that inferential statistics happens to be a rather efficient and inexpensive approach (when one is interested in drawing certain conclusions) in comparison to descriptive statistics.
References
McGregor, S.L. (2017). Understanding and Evaluating Research: A Critical Guide. SAGE Publications.
Pyrczak, F. (2016). Making Sense of Statistics: A Conceptual Overview. Taylor & Francis (6th ed.). Routledge.