<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">The widespread misuse and misrepresentation of statistical information often gives the field a bad name. Some say that &ldquo;numbers don&rsquo;t lie,&rdquo; but the people who use numbers to support their claims often do.</span></span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">A recent investigation of famous social psychologist, Diederik Stapel, has led to the retraction of his articles from some of the world&rsquo;s top journals including, </span><em><span style="color:black">Journal of Experimental Social Psychology, Social Psychology, Basic and Applied Social Psychology, British Journal of Social Psychology, </span></em><span style="color:black">and the magazine </span><em><span style="color:black">Science</span></em><span style="color:black">. Diederik Stapel is a former professor at Tilburg University in the Netherlands. Over the past two years, an extensive investigation involving three universities where Stapel has worked concluded that the psychologist is guilty of fraud on a colossal scale. Falsified data taints over 55 papers he authored and 10 Ph.D. dissertations that he supervised.</span></span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px"><em><span style="color:black">Stapel did not deny that his deceit was driven by ambition. But it was more complicated than that, he told me. He insisted that he loved social psychology but had been frustrated by the messiness of experimental data, which rarely led to clear conclusions. His lifelong obsession with elegance and order, he said, led him to concoct results that journals found attractive. &ldquo;It was a quest for aesthetics, for beauty&mdash;instead of the truth,&rdquo; he said. He described his behavior as an addiction that drove him to carry out acts of increasingly daring fraud</span></em><span style="color:black">.</span></span></p>

<p style="text-align:justify">&nbsp;</p>

<p><span style="font-size:14px"><span style="color:black">The committee investigating Stapel concluded that he is guilty of several practices including</span></span></p>

<p><span style="font-size:14px"><span style="color:black">&bull; creating datasets, which largely confirmed the prior expectations,</span></span></p>

<p><span style="font-size:14px"><span style="color:black">&bull; altering data in existing datasets,</span></span></p>

<p><span style="font-size:14px"><span style="color:black">&bull; changing measuring instruments without reporting the change, and</span></span></p>

<p><span style="font-size:14px"><span style="color:black">&bull; misrepresenting the number of experimental subjects.</span></span></p>

<p>&nbsp;</p>

<p><span style="font-size:14px"><span style="color:black">Clearly, it is never acceptable to falsify data the way this researcher did. Sometimes, however, violations of ethics are not as easy to spot.</span></span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">Researchers have a responsibility to verify that proper methods are being followed. The report describing the investigation of Stapel&rsquo;s fraud states that, &ldquo;statistical flaws frequently revealed a lack of familiarity with elementary statistics.&rdquo; Many of Stapel&rsquo;s co-authors should have spotted irregularities in his data. Unfortunately, they did not know very much about statistical analysis, and they simply trusted that he was collecting and reporting data properly.</span></span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">Many types of statistical fraud are difficult to spot. Some researchers simply stop collecting data once they have just enough to prove what they had hoped to prove. They don&rsquo;t want to take the chance that a more extensive study would complicate their lives by producing data contradicting their hypothesis.</span></span></p>

<p>&nbsp;</p>

<p><span style="font-size:14px"><span style="color:black">Professional organizations, like the American Statistical Association, clearly define expectations for researchers. There are even laws in the federal code about the use of research data.</span></span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">When a statistical study uses human participants, as in medical studies, both ethics and the law dictate that researchers should be mindful of the safety of their research subjects. The U.S. Department of Health and Human Services oversees federal regulations of research studies with the aim of protecting participants. When a university or other research institution engages in research, it must ensure the safety of all human subjects. For this reason, research institutions establish oversight committees known as </span><strong><span style="color:black">Institutional Review Boards (IRB)</span></strong><span style="color:black">. All planned studies must be approved in advance by the IRB.</span></span></p>

<p>&nbsp;</p>

<p><span style="font-size:14px"><span style="color:black">Key protections that are mandated by law include the following:</span></span></p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">&bull; Risks to participants must be minimized and reasonable with respect to projected benefits.</span></span></p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">&bull; Participants must give </span><strong><span style="color:black">informed consent</span></strong><span style="color:black">. This means that the risks of participation must be clearly explained to the subjects of the study. Subjects must consent in writing, and researchers are required to keep documentation of their consent.</span></span></p>

<p><span style="font-size:14px"><span style="color:black">&bull; Data collected from individuals must be guarded carefully to protect their privacy.</span></span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">These ideas may seem fundamental, but they can be very difficult to verify in practice. Is removing a participant&rsquo;s name from the data record sufficient to protect privacy? Perhaps the person&rsquo;s identity could be discovered from the data that remains. What happens if the study does not proceed as planned and risks arise that were not anticipated? When is informed consent really necessary? Suppose your doctor wants a blood sample to check your cholesterol level. Once the sample has been tested, you expect the lab to dispose of the remaining blood. At that point the blood becomes biological waste. Does a researcher have the right to take it for use in a study?</span></span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">It is important that students of statistics take time to consider the ethical questions that arise in statistical studies. How prevalent is fraud in statistical studies? You might be surprised&mdash;and disappointed. Vigilance against fraud requires knowledge. Learning the basic theory of statistics will empower you to analyze statistical studies critically.</span></span><span style="font-size:14px"><span style="color:white">Exam</span></span></p>

<p><span style="font-size:14px"><span style="color:white">Example 1.23</span></span></p>

<p><span style="font-size:14px"><strong>Example 1.24</strong></span></p>

<p>&nbsp;</p>

<p><span style="font-size:14px"><span style="color:black">Describe the unethical behavior in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected.</span></span></p>

<p>&nbsp;</p>

<p><span style="font-size:14px"><span style="color:black">A researcher is collecting data in a community.</span></span></p>

<p><span style="font-size:14px"><span style="color:black">a. She selects a block where she is comfortable walking because she knows many of the people living on the street.</span></span></p>

<p><span style="font-size:14px"><span style="color:black">b. No one seems to be home at four houses on her route. She does not record the addresses and does not return at a later time to try to find residents at home.</span></span></p>

<p><span style="font-size:14px"><span style="color:black">c. She skips four houses on her route because she is running late for an appointment. When she gets home, she fills in the forms by selecting random answers from other residents in the neighborhood.</span></span></p>

<p>&nbsp;</p>

<p><span style="font-size:14px"><strong>Solution 1.24</strong></span></p>

<p>&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">a. By selecting a convenient sample, the researcher is intentionally selecting a sample that could be biased. Claiming that this sample represents the community is misleading. The researcher needs to select areas in the community at random.</span></span></p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">b. Intentionally omitting relevant data will create bias in the sample. Suppose the researcher is gathering information about jobs and child care. By ignoring people who are not home, she may be missing data from working families that are relevant to her study. She needs to make every effort to interview all members of the target sample.</span></span></p>

<p style="text-align:justify"><span style="font-size:14px"><span style="color:black">c. It is never acceptable to fake data. Even though the responses she uses are </span><em><span style="color:black">real </span></em><span style="color:black">responses provided by other participants, the duplication is fraudulent and can create bias in the data. She needs to work diligently to interview everyone on her route.</span><span style="color:white">pl</span></span></p>