Descriptive Statistics

Posted: August 26th, 2021

Assignment 1: Descriptive Statistics

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Assignment 1: Descriptive Statistics

Introduction

            In the article Research Methods Knowledge Base: Descriptive Statistics,the author shares vital pieces of information regarding the subject of descriptive statistics. Beyond the theoretical aspects of this subject, the author goes a step further to explain and demonstrate some of the practical ways of making use of this branch of statistics. Thus, the article is a go-to place for statistics students as well as business leaders who rely on statistics to a large extent in their processes of making moves and decisions. According to the author of this article, descriptive statistics is the branch of statistics that aims at describing a given number of features of data gathered from a scientific study or research (Trochim, 2020). The aim is mainly the provision of a brief and at the same elaborate review of the sample data. Also, it aims at explaining the measures to be performed on a particular study’s data. In its application,therefore, descriptive statistics is usually exhibited through several graphical analyses which form a pertinent part of nearly every type of quantitative data analysis.

Summary

            The article begins by highlighting the meaning of the term Descriptive Statistics as a branch of the broader mathematical field of statistics. It then goes further to explain how descriptive statistics differ from the inferential statistics. To this end, the author explains that descriptive statistics centers around the description of the behavior of the data samples collected from a scientific study, whereas inferential statistics is primarily used for purposes of making inference basing on the data sample at hand to make informed decisions. Moreover, the article explains how the concept of univariate analysis is employed in assessingcases of a single variable at the same time. In this regard, the article explains in great detail the three primary areas that are usually considered. These are the distribution, the measures of central tendency, and dispersion. The conclusion from the article is the importance of making use of descriptive statistics. For instance, it helps to present data in a way that makes it possible for people to easily visualize it. Overly, the author of this article opines that descriptive statistics is an instrumental mechanism of breaking raw data into useful pieces of information that the targeted people can draw from meaningful conclusions.

Descriptive Statistics

            From an academic standpoint, descriptive statistics make use of univariate analysis of a single data variable along with three main criteria. These include distribution, measures of central tendency, and dispersion, which form the essence of descriptive statistics (George & Mallery, 2016). In various creative ways, the article elaborates on how descriptive each of the areas mentioned above is applied. First and foremost, distribution describes the frequency of appearance of several variables within a data set. For example, from the 2019 student enrollment data from Howard University, the article shares that 85 percent of the students are African Americans, 3 percent are Asians, 2.9 percent are whites, and 1.28 percent are Hispanics. Graphs are the commonly adopted ways of displaying the frequency distribution of a given data set.

            The second univariate analysis method employed in descriptive statistics is the use of measures of central tendency. They are three in number, and they include mean, median, and mode (Bonner, 2018). The article states that mean is the commonly used method used in the measure of central tendency. It highlights the average of all the data samples involved in a given study. For example, the article shares that from the examination results of ten students: 50, 77, 63, 67, 35, 46, 21, 92, 46, 88, the mean is 58.5; gotten by summing up the total marks of the students then dividing this sum by 10. By definition, median refers to the variable that is found at the middle of a data set. For example, from the above data set, the median value is (35 + 46)/2 = 40.5. Lastly, the article shares that mode is the commonly occurring value in a given data set. For example, from the above data set, the mode is 46 since it appears twice, unlike other values that occur only once. The spread of variables around a given data set’s central tendency is usually termed as dispersion.There are manyuniversally agreed upon methods of determining dispersion. Examples include variance, range, standard deviation, quartiles, and percentiles.

Analysis and Real-World Application of Descriptive Statistics

            Descriptive statistics is applied in nearly every sphere of human life (Bickel & Lehmann, 2012). From the above discussion, it is clear that the future application of descriptive statistics can transcend the current human imagination. I aspire to be a reputable surveyor in the future; hence, it is necessary not ignore the fact that I will make use of the above-described measures of central tendency in conducting the analysis of forest land cover data for my local area and also for all the other places to which I will be sent.

Conclusion

            In conclusion, descriptive statistics is an essential tool whose application in various spheres of human life keeps expanding by the day. It is instrumental in breaking down of raw data into meaningful pieces of information from which the targeted people can quickly draw the intended message. Every professional should aspire to have a good grasp of the three-univariate analyses employed in descriptive statistics. These include: dispersion, measures of central tendency, and distribution and are broadly discussed above. Overall, descriptive statistics make it possible to keep the original data and the meaning drawn from it undistorted.

References

Bickel, P. J., & Lehmann, E. L. (2012). Descriptive statistics for nonparametric models. III. Dispersion. Selected works of EL Lehmann (pp. 499-518). Springer, Boston, MA.

Bonner, M. D. (2018). Descriptive statistics. Police Abuse in Contemporary Democracies, 257.

George, D., & Mallery, P. (2016). Descriptive statistics. In IBM SPSS Statistics 23 Step by Step (pp. 126-134). Routledge.

Trochim, W., M., K. (2020). Research Methods Knowledge Base: Descriptive Statistics. Retrieved on February 13, 2020, from https://socialresearchmethods.net/kb/descriptive-statistics/