BUS308: Statistics for Mangers
Statistical data has become an item that we see all around us in our everyday lives, from television programs talking about selling products or politicians using data to show how they perform in their jobs, in hopes to be reelected. Throughout the course in Statistics for Managers, I have learned many things on how the use of statistical information can help me to understand these items and also to help me to perform my job and understand the day-to-day operation of the company. With the use of statistics, anyone can find out information and details on most anything, allowing them to understand a business better or to make better decisions in their everyday life. Because statistics is all around us, using and understanding this information is important to find answers to questions, to make better decisions, and understand how things work.
Some of the types of information I have learned to use is through the use of descriptive and inferential statistics. According to the textbook for statistics, “descriptive characteristics can provide a great economy when data sets are large. Inferential statistics are utilized when the sample’s characteristics are important for what they reveal about the entire population”. (Tanner & Youssef-Morgan, 2013) Even though there are many different kinds of descriptive statistics, the ones most used are those that explain a central tendency or a variability within the data. These are the ones that allow us to measure and analyze different information using mean, media, and mode information to draw our conclusions. Inferential statistics is all about “the process of drawing conclusions about population characteristics through analysis of the sample data” (Dell, 2013). This takes the population of the data and generalizes it to its characteristics to help reveal the details about the remaining population.
From these statistical test, we are looking to create a hypothesis development and testing for the information we are trying to discover. This testing will help us find out if the predictions of the data will be a significant difference from the question or if it is of non-significance from what we are trying to determine. Performing this test will result in a null or an alternate hypothesis type of answer. The null hypothesis tells us that the comparison between the two populations of information has little or no difference. Whereas the alternate hypothesis demonstrates that there is a significance with the data and this can be used to make a determination of the data. This is stated by O’Keefe as “ rejection of the null hypothesis permits one to conclude the a nonrandom process was involved, whereas a failure to reject the null hypothesis means that one cannot rule out the possibility that the observed data arose by chance” (O'Keefe, 2011) Just like our comparison of salaries between males and females during the class, and we are asking the question if there is a difference between the two, a null hypothesis would tell us that there is little difference and an alternate hypothesis would tell us that there is a difference in how the two genders get paid within the company.
In gathering the data and performing the calculations there is also the need to make sure the proper testing is being performed. From this course I have learned how to evaluate the information using the different forms of statistical testing. According to the Journal of Pharmaceutical Negative Results, “performing the statistical tests become easy, but selection of appropriate statistical test is still a problem.” (Jaykaran, 2010) Depending on the data and the questions asked, there may be different ways to perform the test to receive an appropriate answer. Also the questions on differences or associations, the specifics of the data, and the groups involved, will have an impact on which type of testing is the correct one...
References: Bedeian, A. G. (2014). ”More Than Meets the Eye".A Guild to Interpreting the Descriptive Statistics and Correlation Matrices Reported in Management Research. Academy Of Management Learning & Education, 13(1), 121-135. doi:10.5465/amle.2013.0001
Dell, G. (2013). Common Statistical Errors and Mistakes: Valuation and Reliability. Appraisal Journal, 81(4), 332. Retrieved November 26, 2014, from http://eds.a.ebscohost.com.proxy-library.ashford.edu/eds/pdfviewer/pdfviewer?vid=2&sid=c23a3a8f-0480-4102-9ba8-2015b2bf9e49%40sessionmgr4002&hid=4105
Jaykaran. (2010). How to select appropriate statistical test? Journal of Pharmaceutical Negative Results, 1(2), 61-63. doi:10.4103/0976-9234.75708
Nelson, M. K. (1995). Strategies of Auditors: Evaluating of Sample Results. Auditing: A Journal of Practice & Theory, 14(1), 39-49. Retrieved November 26, 2014, from http://eds.a.ebscohost.com.proxy-library.ashford.edu/eds/detail/detail?vid=15&sid=9149df10-8f01-49e6-8f49-20fb94095be0%40sessionmgr4005&hid=4210&bdata=JnNpdGU9ZWRzLWxpdmU%3d#db=bsh&AN=9509264791
O 'Keefe, D. J. (2011). The Asymmetry of Predictive and Descriptive Capabilities in Quantitative Communication Research: Implications for Hypothesis Development and Testing. Communication Methods & Measures, 5(2), 113-125. doi:10.1080/19312458.2011.568375
Tanner, D. E., & Youssef-Morgan, C. M. (2013). Statistics for Managers. San Diego, Ca: Bridgeport Education, Inc.
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