BUS520 Business Analytics and Decision Making (Essay Sample)
Sampling is a major way to collect data, and there are different sampling methods.
Please identify one sampling method and discuss the sampling bias related to this method.
Also explain how the sampling bias could impact the validity and generalizability of data analysis results, as well as the business decision making.
Week 1: Provide your initial discussion post to the question. Be sure to include references to any resources you used. You should use at least one resource to help you with your initial discussion.
Alexander, H., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics. Openstax. Retrieved from https://openstax(dot)org/details/books/introductory-business-statistics
For Module 1, you should read through the following material in this textbook:
Chapter 1: Sampling and Data
This chapter provides a general overview of statistics, data terminology, and sampling techniques.
Chapter 2: Descriptive Statistics
This chapter explains measures of central tendency, including mean, median, and mode. It also carefully discusses statistics that measure variation, including variance and standard deviation and covers important concepts such as skewness.
As mentioned in the overview, it is very important that you become comfortable with Microsoft Excel. This course, along with many others in the graduate program, uses Excel extensively. It is also very likely that you will use Excel in the workplace. Below are important videos that will help you get started with some of the basics.
The sampling methods selected by researchers can either be probabilistic or non-probabilistic. In probability sampling, the chances of an element being selected from the population into the sample are known, while the chances in non-probability sampling are not known (Martinez-Mesa, et al., 2016). In this paper, I will discuss the cluster sampling method, which falls under probabilistic sampling. In cluster sampling, the elements of the population are divided into groups, which are referred to as clusters, and each cluster is treated as a sample unit (Sekaran & Bougie, 2016). The clusters are then selected randomly from the population to form a representative sample. This method is effective when the population is scattered and difficult to access, either due to cost or time constraints (Rahi, 2017). Each cluster has a number of elements, which make up the cluster size.
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