QNT 351 Week 1 Statistics in Business

Statistics in Business

Statistics is an important facet of modern business decision making. Statistics is defined as “The science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions” (Lind, Marchal, & Wathen, 2011). There are two primary forms of statistical research which include: descriptive and inferential statistics.  Both of these statistical methods comprise the majority of business research.

Descriptive Statistics

Descriptive statistics are methods of data analysis that attempt to present patterns in data. These patterns are used to extrapolate conclusions concerning the subject of the research or the hypothesis. Descriptive statistics is a method of organizing and studying data that provides a meaningful perspective  (AERD, 2014).   For example, descriptive statistics can be used by business researchers to discover patterns in data such as worker efficiency and productivity. Descriptive statistics can organize the frequency distribution of numbers pertaining to efficiency and productivity. Using time and product output, a sample size of workers can be measured with scores ranging from 0-10 with 5 being the either the mode, median, or mean (AERD, 2014). Measures such as this can be used by managers to graph average productivity and establish where productivity falls in relation to the central position.

Inferential Statistics

Inferential statistics works differently than descriptive statistics as it analyzes information concerning an immediate data group. For example, the same average rate of productivity for a specific job such as painter in a factory, can be discovered by studying a sample of workers who paint.  By studying a small population the same averages for productivity and efficiency can be extrapolated for the painting workforce  (AERD, 2014). For example, if 200 assembly line painters are studied with regard to productivity and efficiency then the results can be applied across the entire population of assembly line painters in the company (AERD, 2014). This provides a specific set of data for a specific population.

Conclusions

While both statistical methods are beneficial there are situations in which one may be better than another. For instance, when management needs to determine trends in the workforce large amounts of data may be required for analysis. In this instance, differential statistics will be more useful. Similarly, when studying large workforces, inferential statistics is a better method because it is less time consuming and less expensive. The choice of these different research methods depends on the data and type of research being committed.

References

AERD. (2014). Descriptive and Inferential Statistics. Retrieved from AERD: https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php

Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and economics (7th ed.). New York, NY: McGraw-Hill/Irwin.

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