# 12-EBR-CH11.pdf - Part Data Analysis and Reporting 4...

• Notes
• 20

This preview shows page 1 - 5 out of 20 pages.

##### We have textbook solutions for you!
The document you are viewing contains questions related to this textbook.
The document you are viewing contains questions related to this textbook.
Chapter 18 / Exercise 18.43
Chemistry for Today: General, Organic, and Biochemistry
Seager/Slabaugh
Expert Verified
PART Data Analysis and Reporting 4 CHAPTER 11 Analyzing and Interpreting Data for Decisions CHAPTER 12 Analyzing Qualitative and Big Data CHAPTER 13 Advanced Data Analysis CHAPTER 14 The Research Report Mmaxer/Shutterstock.com
##### We have textbook solutions for you!
The document you are viewing contains questions related to this textbook.
The document you are viewing contains questions related to this textbook.
Chapter 18 / Exercise 18.43
Chemistry for Today: General, Organic, and Biochemistry
Seager/Slabaugh
Expert Verified
193 Learning Objectives Upon completing this chapter, you should understand: The relationship between data analysis and decision making. The importance of planning the data analysis procedures to be used on the collected data. How frequency distributions, measures of central tendency, and dispersion help in summarizing and understanding data. The usefulness of cross tabulations in data analysis to understand underlying differences in responses. CHAPTER Analyzing and Interpreting Data for Decisions 11 Chapter Outline From Data to Decisions Data Summary Methods Cross-Tabulation Advanced Analytical Techniques Summary Key Terms Discussion Questions Endnotes Alexander Lukatsky/Shutterstock.com
194 Part 4 Data Analysis and Reporting Hamlet: Do you see yonder cloud that’s almost in shape of a camel? Polonius: By the mass, and ’tis like a camel indeed. Hamlet: Methinks it is like a weasel. Polonius: It is backed like a weasel. Hamlet: Or like a whale? Polonius: Very like a whale. -Hamlet , Act III, Scene ii Data , like cloud formations, can be made to appear to support any number of conjectures. The fertile mind of the analyst can “see” conclusions to the data that may be more the creation of the imagination than an objective reading of the information generated by the research. This gives pause to the researcher or analyst, because it implies that, while there may be an ul- timate truth, the analyst’s personal agenda, perceptual incli- nations, experience, and even personality can influence the interpretation of the research study’s results. Nevertheless, we must use all the means at our disposal to arrive at the most objective, concise, but also thorough analysis possible of the data generated by the research. However, the same observa- tion of objective data may be subject to multiple interpretations. Consider the following apocryphal story: An American shoe company sent three researchers to a Pacific island to see if the company could sell its shoes there. Upon returning they made a presentation to management. The first researcher summed up the findings this way. “The people there don’t wear shoes. There is no market.” The second researcher said: “The people there don’t wear shoes. There is a tremendous market!” The third researcher reported: “The people there don’t wear shoes. However, they have bad feet and could benefit from wearing shoes. We would need to redesign our shoes, however, because they have smaller feet. We would have to educate the people about the benefits of wearing shoes. We would need to gain the tribal chief’s cooperation. The people don’t have any money, but they grow great pineapples. I’ve estimated the sales potential over a three-year period and all of our costs including selling the pineapples to a European supermarket chain, and con-