Lecture Data ans statistics Applications in Business and Economics

1.8 Ethical Guidelines for Statistical Practice
Ethical behavior is something we should strive for in all that we do. Ethical issues arise in
statistics because of the important role statistics plays in the collection, analysis, presentation,
and interpretation of data. In a statistical study, unethical behavior can take a variety
of forms including improper sampling, inappropriate analysis of the data, development of
misleading graphs, use of inappropriate summary statistics, and/or a biased interpretation
of the statistical results.
As you begin to do your own statistical work, we encourage you to be fair, thorough,
objective, and neutral as you collect data, conduct analyses, make oral presentations, and
present written reports containing information developed. As a consumer of statistics, you
should also be aware of the possibility of unethical statistical behavior by others. When you
see statistics in newspapers, on television, on the Internet, and so on, it is a good idea to
view the information with some skepticism, always being aware of the source as well as the
purpose and objectivity of the statistics provided.
The American Statistical Association, the nation’s leading professional organization for
statistics and statisticians, developed the report “Ethical Guidelines for Statistical Practice”1
to help statistical practitioners make and communicate ethical decisions and assist students
in learning how to perform statistical work responsibly. The report contains 67 guidelines
organized into eight topic areas: Professionalism; Responsibilities to Funders, Clients, and
Employers; Responsibilities in Publications and Testimony; Responsibilities to Research
Subjects; Responsibilities to Research Team Colleagues; Responsibilities to Other Statisticians
or Statistical Practitioners; Responsibilities Regarding Allegations of Misconduct;
and Responsibilities of Employers Including Organizations, Individuals, Attorneys, or
Other Clients Employing Statistical Practitioners.
One of the ethical guidelines in the professionalism area addresses the issue of running
multiple tests until a desired result is obtained. Let us consider an example. In Section 1.5 we
discussed a statistical study conducted by Norris Electronics involving a sample of 200 highintensity
lightbulbs manufactured with a new filament. The average lifetime for the sample,
76 hours, provided an estimate of the average lifetime for all lightbulbs produced with the new
filament. However, consider this. Because Norris selected a sample of bulbs, it is reasonable
to assume that another sample would have provided a different average lifetime.
Suppose Norris’s management had hoped the sample results would enable them to
claim that the average lifetime for the new lightbulbs was 80 hours or more. Suppose further
that Norris’s management decides to continue the study by manufacturing and testing
repeated samples of 200 lightbulbs with the new filament until a sample mean of 80 hours
or more is obtained. If the study is repeated enough times, a sample may eventually be
obtained—by chance alone—that would provide the desired result and enable Norris to
make such a claim. In this case, consumers would be misled into thinking the new product
is better than it actually is. Clearly, this type of behavior is unethical and represents a gross
misuse of statistics in practice.
Several ethical guidelines in the responsibilities and publications and testimony area deal
with issues involving the handling of data. For instance, a statistician must account for all data
considered in a study and explain the sample(s) actually used. In the Norris Electronics study
the average lifetime for the 200 bulbs in the original sample is 76 hours; this is considerably
less than the 80 hours or more that management hoped to obtain. Suppose now that after reviewing
the results showing a 76 hour average lifetime, Norris discards all the observations
with 70 or fewer hours until burnout, allegedly because these bulbs contain imperfections
caused by startup problems in the manufacturing process. After discarding these lightbulbs,
the average lifetime for the remaining lightbulbs in the sample turns out to be 82 hours.Would
you be suspicious of Norris’s claim that the lifetime for their lightbulbs is 82 hours?
If the Norris lightbulbs showing 70 or fewer hours until burnout were discarded to simply
provide an average lifetime of 82 hours, there is no question that discarding the lightbulbs
with 70 or fewer hours until burnout is unethical. But, even if the discarded lightbulbs contain
imperfections due to startup problems in the manufacturing process—and, as a result,
should not have been included in the analysis—the statistician who conducted the study
must account for all the data that were considered and explain how the sample actually used
was obtained. To do otherwise is potentially misleading and would constitute unethical
behavior on the part of both the company and the statistician.
A guideline in the shared values section of the American Statistical Association report
states that statistical practitioners should avoid any tendency to slant statistical work toward
predetermined outcomes. This type of unethical practice is often observed when unrepresentative
samples are used to make claims. For instance, in many areas of the country smoking
is not permitted in restaurants. Suppose, however, a lobbyist for the tobacco industry
interviews people in restaurants where smoking is permitted in order to estimate the percentage
of people who are in favor of allowing smoking in restaurants. The sample results
show that 90% of the people interviewed are in favor of allowing smoking in restaurants.
Based upon these sample results, the lobbyist claims that 90% of all people who eat in restaurants
are in favor of permitting smoking in restaurants. In this case we would argue that only
sampling persons eating in restaurants that allow smoking has biased the results. If only the
final results of such a study are reported, readers unfamiliar with the details of the study (i.e.,
that the sample was collected only in restaurants allowing smoking) can be misled.
The scope of the American Statistical Association’s report is broad and includes ethical
guidelines that are appropriate not only for a statistician, but also for consumers of statistical
information.We encourage you to read the report to obtain a better perspective of ethical issues
as you continue your study of statistics and to gain the background for determining how
to ensure that ethical standards are met when you start to use statistics in practice.
Summary
Statistics is the art and science of collecting, analyzing, presenting, and interpreting data.
Nearly every college student majoring in business or economics is required to take a course
in statistics. We began the chapter by describing typical statistical applications for business
and economics.
Data consist of the facts and figures that are collected and analyzed. Four scales of
measurement used to obtain data on a particular variable include nominal, ordinal, interval,
and ratio. The scale of measurement for a variable is nominal when the data are labels or
names used to identify an attribute of an element. The scale is ordinal if the data demonstrate
the properties of nominal data and the order or rank of the data is meaningful. The
scale is interval if the data demonstrate the properties of ordinal data and the interval
between values is expressed in terms of a fixed unit of measure. Finally, the scale of measurement
is ratio if the data show all the properties of interval data and the ratio of two
values is meaningful.
For purposes of statistical analysis, data can be classified as categorical or quantitative.
Categorical data use labels or names to identify an attribute of each element. Categorical
data use either the nominal or ordinal scale of measurement and may be nonnumeric or
numeric. Quantitative data are numeric values that indicate how much or how many. Quantitative
data use either the interval or ratio scale of measurement. Ordinary arithmetic operations
are meaningful only if the data are quantitative. Therefore, statistical computations
used for quantitative data are not always appropriate for categorical data.
In Sections 1.4 and 1.5 we introduced the topics of descriptive statistics and statistical
inference. Descriptive statistics are the tabular, graphical, and numerical methods used to
summarize data. The process of statistical inference uses data obtained from a sample
to make estimates or test hypotheses about the characteristics of a population. The last three
sections of the chapter provide information on the role of computers in statistical analysis,
an introduction to the relative new field of data mining, and a summary of ethical guidelines
for statistical practice.