Producing Data
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CHAPTER OUTLINE
- 3.1 Sources of Data
- 3.2 Designing Samples
- 3.3 Designing Experiments
- 3.4 Data Ethics
Introduction
Reliable data are needed to make business decisions. Here are some examples where carefully collected data are essential.
- How does General Motors decide the numbers of vehicles of different colors that it will produce?
- How will Whole Foods choose a location for a new store?
- How does Monsanto decide how much it is willing to spend for a Super Bowl commercial?
In Chapters 1 and 2 we learned some basic tools of data analysis. We used graphs and numbers to describe data. When we do exploratory data analysis, we rely heavily on plotting the data. We look for patterns that suggest interesting conclusions or questions for further study. However, exploratory analysis alone can rarely provide convincing evidence for its conclusions because striking patterns we find in data can arise from many sources.
exploratory data analysis
The validity of the conclusions that we draw from an analysis of data depends not only on the use of the best methods to perform the analysis but also on the quality of the data. Therefore, Section 3.1 begins this chapter with a short overview on sources of data. The two main sources for quality data are designed samples and designed experiments. We study these two sources in Sections 3.2 and 3.3, respectively.
Should an experiment or sample survey that could possibly provide interesting and important information always be performed? How can we safeguard the privacy of subjects in a sample survey? What constitutes the mistreatment of people or animals who are studied in an experiment? These are questions of ethics. In Section 3.4, we address ethical issues related to the design of studies and the analysis of data.
ethics