Conspiracy theories are easy to propagate and difficult to refute. Having long flourished in politics and religion, they have also spread into science and medicine. It is useful to think of conspiracy theorizing as a meme, a cultural invention that passes from one mind to another and thrives, or declines, through a process analogous to genetic selection (Dawkins 1976). The conspiracy meme competes with other rhetorical memes, such as the fair debate meme, the scientific expertise meme, and the resistance to orthodoxy meme.

The central logic of the conspiracy meme is to question, often on speculative grounds, everything the “establishment” says or does and to demand immediate, comprehensive, and convincing answers to all questions. Unconvincing answers are taken as proof of conspiratorial deception. A good example is the film Loose Change 9/11: An American Coup (Avery 2009), which started out as a short fictional 2005 video about the World Trade Center attacks that was marketed as if it were a truth-seeking documentary. The 2005 video went viral on the Internet and has been viewed by over ten million people. Loose Change raises a long series of questions illustrated by tendentious information, such as the fact that the fires in the World Trade Center were not hot enough to melt steel. But no one had claimed that the steel had melted, only that it had gotten hot enough to weaken and collapse, which it did. The video presents the fact that the U.S. Internal Revenue Service (IRS) is keeping certain people's tax returns secret, set to an ominous musical background suggestive of evildoing-despite the well-known fact that the IRS keeps everyone's tax returns secret.

When an alleged fact is debunked, the conspiracy meme often just replaces it with another fact. One of the producers of Loose Change, Korey Rowe, stated, “We don't ever come out and say that everything we say is 100 percent [correct]. We know there are errors in the documentary, and we've actually left them in there so that people [will] discredit us and do the research for themselves” (Slensky 2006).

When the conspiracy meme is reinforced by a regular diet of “alternative” videos and one-sided literature, it can become a habitual way of thinking. People who believe in one conspiracy are more likely to believe in others (Goertzel 1994; Kramer 1998). A young self-declared conspiracy theorist challenged me to debate one conspiracy theory per week with him, including theories about genetically modified (GM) foods, vaccine neurotoxins, AIDS, and September 11, 2001. He expressed his “true belief” that there is a “kernel of truth” in almost every conspiracy theory and claimed that once you understand the kernel, all you have to do is “connect the dots to make a picture.”

Conspiracy theorists have connected a lot of dots. The ninety-two conspiracy theories described in a recent handbook (McConnachie and Tudge 2008) range in topic from Tutankhamen and the curse of the pharaoh, the Protocols of the Elders of Zion, and satanic ritual abuse to the alleged scheming of the Council on Foreign Relations, the Trilateral Commission, and the British royal family. Other theories involve religious cults, alien abductions, or terrorist plots. Some are merely amusing, but others have fueled wars, inquisitions, and genocides in which millions of people died.

Scientific and technological conspiracies often allege the misuse of science by government, the military, or large corporations, and they include bizarre claims that the military suppressed technology that could make warships invisible, automobile or oil companies possess hidden technology that can turn water into gasoline, and the military is secretly in cahoots with space aliens. Conspiracy theorists have argued that the AIDS virus was deliberately created as part of a plot to kill black or gay people, the 1969 Moon landing was staged in a movie studio, and dentists seek to poison Americans by fluoridating public water supplies. Other theorists claim that corporate officers and public health officials suppress evidence that preservatives in vaccines cause autism and silicone breast implants cause connective-tissue disease (Specter 2009; Wallace 2009).

Conspiracy theories include claims that a major drug company hid reports stating that its leading anti-inflammatory drug caused heart attacks and strokes (Specter 2009) and that environmental scientists have conspired to keep refereed journals from publishing papers by researchers skeptical that global warming is a crisis (Hayward 2009; Revkin 2009). There are many theories about physicians or drug companies conspiring to suppress non-mainstream medical treatments, vitamins, and health foods. One author alleges that big business and the medical establishment conspired to obstruct the search for a cure for AIDS so that they could sell their ineffective drugs and treatments (Nussbaum 1990).

Many of these theories are clearly absurd, but some are plausible and others actually contain elements of truth. How can we distinguish among the amusing eccentrics, the honestly misguided, the avaricious litigants, and the serious skeptics questioning a premature consensus? With scientific claims, the only definitive answer is to reexamine the original research data and repeat the experiments and analysis. But no one has the time or the expertise to examine the original research literature on every topic, let alone repeat the research. As such, it is important to have some guidelines for deciding which theories are plausible enough to merit serious examination.

One valuable guideline is to look for cascade logic in conspiracy arguments (Susstein and Vermeule 2008). This occurs when defenders of a conspiracy theory find it necessary to implicate more and more people whose failure to discover or reveal the conspiracy can be explained only by their alleged complicity. Another guideline is to look for exaggerated claims about the power of the conspirators, claims that are needed to explain how they were able to intimidate so many people and cover their tracks so well. The more vast and powerful the alleged conspiracy, the less likely that it could have remained undiscovered.

For example, the claim that the Moon landing in 1969 was a hoax implies the complicity of thousands of American scientists and technicians, as well as that of Soviet astronomers and others around the world who tracked the event. It is incredibly implausible that such a conspiracy could have held together. On the other hand, the theory that a few individuals in Richard Nixon's campaign conspired to break into their opponents' offices in the Watergate building was plausible and proved worth investigating. Similarly, the theory that a group of climate scientists conspired to suppress research that they believed to be misleading and harmful to public policy is plausible and worth investigating, despite the small likelihood that such a conspiracy would remain undetected for long.

Definition of ‘Conspiracy'

The conspiracy meme works because conspiracies do exist in the real world. Claims of conspiracy cannot be reflexively dismissed, but they are difficult to test because lack of evidence can be interpreted as proof of how cleverly the conspirators have hidden it. The first step in testing claims of conspiracy is to establish precisely what is being claimed. There is no single accepted definition of “conspiracy,” and people apply the term differently depending on their point of view. The Oxford English Dictionary defines a conspiracy quite loosely as “an agreement between two or more persons to do something criminal, illegal, or reprehensible.” There are legal definitions of criminal conspiracy, but whether something is “reprehensible” is in the eye of the beholder. When Hillary Clinton protested that her husband was the victim of a “vast right-wing conspiracy” and Lyndon Johnson accused the media and liberal activists of a “conspiracy” to oppose his Vietnam War policies, these claimants were intentionally vague as to whether they referred to illegal or merely reprehensible behavior (Kramer and Gavrieli 2005). Any group of people organizing for a cause the speaker does not like may be denounced as “conspirators.”

But the word conspiracy also usually implies something that is secret and hidden. Pigden (2006, 20) defines a conspiracy as “a secret plan on the part of a group to influence events in part by covert action.” Conspiracies so defined certainly do take place, and it may be that the most successful ones are never discovered. They include the (failed) conspiracy to assassinate Adolph Hitler; the September 11, 2001, terrorist attacks; and the Watergate conspiracy. But the term “conspiracy theory” usually refers to claims that important events have been caused by conspiracies that have heretofore remained undiscovered (Coady 2006). The claim that the World Trade Center was bombed by al-Qaeda would not be a conspiracy theory in this sense, but the claim that it was bombed by Israeli agents or that American authorities knew about it in advance would be. There is no chance of getting agreement on an “official” definition, but people alleging conspiracy should be challenged to be clear about their meaning.

The conspiracy meme flourishes best in politics, religion, and journalism, where practitioners can succeed by attracting followers from the general public. It isn't essential that practitioners actually believe the theory; they may just find it plausible and useful to raise doubts and discredit their competitors. But this strategy should not be enough for scientists. Scientific findings are just that-findings, not speculations about undiscovered goings-on. These findings must be replicable by other scientists.

In their routine work, scientists have little use for the conspiracy meme because success in scientific careers comes from winning grant applications and publishing significant findings in peer-reviewed journals. Attacking other scientists as conspirators would not be helpful for most scientists' careers, however frustrated they may be with referees, editors, colleagues, or administrators who turn down their manuscripts or grant proposals or deny them tenured jobs. But the conspiracy meme may be useful for scientists who are so far out of the mainstream in their field that they seek to appeal to alternative funding sources or publication outlets. The conspiracy meme also occasionally surfaces when a scientist's mental health deteriorates to the point that he or she loses touch with reality.

Trial lawyers, on the other hand, have a great deal of use for the conspiracy meme because they succeed by convincing juries. It is part of the standard repertoire of memes they use to discredit evidence offered by “experts” of all kinds, including scientists. Lawyers focus on the motivations of the experts, on who hired them, on what they are being paid for their testimony, and so on. They also seek out an “expert” who will testify on their side, implying that expertise is for sale to the highest bidder and that opinion is divided on the issue in question. The rewards can be very great if a class-action lawsuit results in a settlement against a wealthy corporation.

Vaccine Conspiracies

Conspiracy theories about vaccines were given a tremendous boost when the esteemed medical journal the Lancet published a study reporting a hypothesized link between the measles-mumps-rubella (MMR) vaccine and autism (Burgess et al. 2006). The media highlighted the story despite the study's very small sample size and speculative causal inferences, and the public reaction was much larger than medical and public health authorities anticipated. Reasons for the public reaction included resentment of pressure on parents, distrust of medical authorities, and the potentially catastrophic nature of possible risk to a vulnerable population. There was also the potential for large class-action settlements in favor of parents who believed their children were injured by the vaccines, some of whom desperately needed help to care for autistic children.

The result was a decline in the proportion of parents having their children vaccinated and a subsequent increase in disease, especially in the United Kingdom. The authorities responded by citing findings from large epidemiologic studies, but much of the press coverage highlighted anecdotal accounts and human-interest stories. Recovery of public confidence in vaccination may be due more to revelations of conflicts of interest on the part of the physician who published the original article-which was eventually withdrawn by the journal-than to the overwhelming evidence for the lack of a relationship between vaccination and autism rates.

Conspiracy theorists typically overlook lapses in logic and evidence by their supporters, but they are quick to pounce on any flaw on the part of their opponents. When a leading Danish vaccine researcher was accused of stealing funds from his university, the vaccine conspiracy theorists pounced. Robert F. Kennedy, Jr., son of a former U.S. Attorney General, used the occasion to denounce the “vaccine cover-up” on the influential blog Huffington Post (Kennedy 2010). He explained away the research findings on vaccines and autism on the grounds that there had been a change in the Danish law and the opening of a new autism clinic. He criticized vaccine researchers for receiving money from the Centers for Disease Control and Prevention (CDC) for their studies and for “being in cahoots with CDC officials intent on fraudulently cherry-picking facts to prove vaccine safety.” But if the CDC had not funded this research, largely in response to popular concerns, vaccine opponents would have denounced it for not doing so.

Genetically Modified Food Conspiracies

Public alarm about GM foods was aroused when a scientist, Árpád Pusztai, claimed in a television interview that rats had suffered intestinal damage due to eating GM potatoes (“Genetically modified” 2010; Enserink 1999). The finding was clearly preliminary; there were only six rats in each of two groups, and one group was fed GM potatoes for only ten days. The reported effects on the rats were minor, but the study received tremendous publicity because it fed into fears that had long been cultivated by environmentalist and anti- capitalist social movements. As the controversy progressed, questions were raised about the integrity of the study, leading Pusztai to leave his research institute. But anti-GM activists denounced criticisms of the research as a conspiracy and circulated a petition among scientists supporting Pusztai's rights. Finally, the Lancet published his study, which had not yet appeared in a refereed journal. They sent it to six reviewers, only one of whom opposed publication. But one of the reviewers who favored publication said he “deemed the study flawed but favored publication to avoid suspicions of a conspiracy against Pusztai and to give colleagues a chance to see the data for themselves” (Enserink 1999).

By releasing his findings on television, Pusztai received extraordinary attention for a study that otherwise might never have been accepted by a leading scientific journal. At least, that was the opinion of the editor of a competing journal who asked “when was the last time [the Lancet] published a rat study that was uninterpretable? This is really lowering the bar” (Enserink 1999). Releasing controversial findings on the Internet or through press releases is justified as a way of making important discoveries available quickly, but it also serves to circumvent the normal scientific review process. Sometimes these “findings,” such as the claim that the decline in crime in the United States in the 1990s was due to the legalization of abortion in the 1970s, become part of the conventional wisdom before other scientists have a chance to debunk them (Zimring 2006).

The Fair Debate Meme

Dissenters from mainstream science often invoke the meme that there are two sides to every question and each side is entitled to equal time to present its case. George W. Bush famously suggested that students be taught both evolution and creationism so that they can judge which has the most convincing argument. Similarly, holocaust deniers demand equal time for their side of the argument, and they might travel to Tehran or wherever they can find a receptive audience. If these dissenters, or “revisionists,” succeed in getting an opportunity to present their case, they will hammer away at any gaps or contradictions in the evidence presented by mainstream researchers, using rhetoric that questions their opponents' motivations while avoiding any hint of weakness or bias in their own case.

This advocacy meme is widely used in law courts and political debates, and it can work well when the question at hand is one of taste or morality. It doesn't work well for scientists because there are objectively right and wrong answers to most scientific questions-they can't be resolved by votes of schoolchildren. Schoolchildren in 1945 might have agreed with U.S. Admiral William Leahy's famous statement that “the [atomic] bomb will never go off, and I speak as an expert on explosives.” But once the bomb went off, there were no longer two sides to the question.

The Scientific Expertise Meme

In deciding to pursue the atomic bomb project, President Harry Truman relied on another meme that is very powerful in western societies, that of reliance on scientific expertise. Decision makers and the general public are most likely to be persuaded by this meme when scientists are in agreement and when their advice and policy prescriptions have a good track record. There is an inherent tension between the policy makers' desire for consensus and the scientists' need to remain open to alternative theories and evidence. Scientists who wish to influence policy may be tempted to claim a scientific consensus when the facts do not yet warrant one.

We social scientists have forfeited much of our potential influence because we are too often perceived as advocates for a cause rather than as objective researchers. Our ability to predict policy outcomes is very limited, yet we sometimes fall into the trap of claiming to know more than we do. Econometricians have been publishing conflicting analyses of the relationship between capital punishment and homicide rates for decades without making any real progress, yet they continue to use their findings to advocate for or against capital punishment (Goertzel and Goertzel 2008). When President Bill Clinton proposed welfare reform in the United States, social scientists specializing in the topic almost universally predicted that a disastrous increase in poverty and hunger would result. In some cases they defended their predictions with elaborate statistical models, despite the fact that these models had no demonstrated track record for predicting trends in poverty (Goertzel 1998). President Clinton deferred to politicians and conservative activists who predicted that poverty and dependency would decline as, in fact, they did.

Memes Collide: HIV/AIDS Deniers

The conflict between the fair debate meme and the scientific expertise meme was pronounced in the dispute between the late Nature editor John Maddox and biologist Peter Duesberg, who opposes the theory that HIV causes AIDS. Relying on the norms of fairness in debate, Duesberg (1995) sought the right to reply to scientific papers that defend mainstream views about the HIV-AIDS connection. At a certain point in the debate, Maddox refused to continue to give Duesberg “the right of reply,” arguing that Duesberg had “forfeited the right to expect answers by his rhetorical technique. Questions left unanswered for more than about ten minutes he takes as further proof that HIV is not the cause of AIDS. Evidence that contradicts his alternative drug hypothesis on the other hand is brushed aside.” Maddox argued that Duesberg was not asking legitimate scientific questions but rather making demands and implying, “Unless you can answer this, and right now, your belief that HIV causes AIDS is wrong” (Maddox 1993).

Maddox observed that “Duesberg will not be alone in protesting that this is merely a recipe for suppressing challenges to received wisdom. So it can be. But Nature will not so use it. Instead, what Duesberg continues to say about the causation of AIDS will be reported in the general interest. When he offers a text for publication that can be authenticated, it will if possible be published.” As an editor of a scientific journal, Maddox was justified in saying that he would publish papers that offered new findings, not ones that just picked at unanswered questions in other people's work. But Maddox was realistic in realizing that his refusal to publish additional comments by Duesberg would be portrayed as censorship by believers in the AIDS conspiracy theory.

The Resistance to Orthodoxy Meme

Duesberg and other dissenters also rely on another well-established rhetorical meme, that of the courageous independent scientist resisting orthodoxy. This meme is frequently introduced with the example of Galileo's defense of the heliocentric model of the solar system against the orthodoxy of the Catholic Church. And there are other cases of dissenting scientists who have later been proven right. Thomas Gold (1989) reports confronting the “herd mentality” of science when advancing his theories on the mechanisms of the inner ear and the nature of pulsars as rotating neutron stars, both of which later came to be accepted. This “herd mentality” is not the product of a deliberate conspiracy, although it may be perceived as one. It is a collective behavior phenomenon: a belief is reinforced and becomes part of the conventional wisdom because it is repeated so often. This is why those who offer differing views are important. Being a dissenter from orthodoxy isn't so difficult; the hard part is actually having a better theory than the conventional one. Dissenting theories should be published if they are backed by plausible evidence, but this does not mean giving critics “equal time” to dissent from every finding by a mainstream scientist.

In his response to Duesberg, Maddox refers to the philosophical argument, associated with Karl Popper, that science progresses through falsification of hypotheses. Maddox says, “True, good theories (pace Popper) are falsifiable theories, and a single falsification will bring a good theory crashing down.” But he goes on in the next sentence to implicitly rely on a different philosophy of science, often associated with the work of Imre Lakatos, which is that science normally progresses by correcting and adding to ongoing research programs, not by abandoning them every time a hypothesis fails. Maddox says, “Unanswered questions are not falsifications; rather, they should be the stimulants of further research.”

Scientists do change their ideas in response to new evidence, perhaps more often than people in most walks of life. Linus Pauling abandoned his triple-helix model of DNA as soon as he saw the evidence for the double-helix model. But he never abandoned his advocacy of vitamin C as a treatment for the common cold and cancer, no matter how many studies failed to show a significant difference between experimental and control groups. Pauling found flaws in each study's research design and insisted that the results would be different if only the study were done differently. He never did any empirical research on vitamin C himself, research that would have risked failing to confirm his hypotheses. He instead limited himself to debunking published scientific studies. Unfortunately, Pauling is probably better known by the general public for this work than for his undisputed and fundamental contributions to chemistry. Pauling did not claim that he was the victim of a conspiracy; he saw himself as challenging the herd mentality of science. But his scientific prestige lent credibility to those who sought to discredit scientific medicine as a conspiracy of doctors and drug companies (Goertzel and Goertzel 1995). Scientific expertise is usually quite specialized, and scientists who advocate for political causes only tangentially related to their area of specialization have no special claim on the truth.

Conspiracy theorists often seem to believe that they can prove a scientific theory wrong by finding even a minor flaw or gap in the evidence for it. Then they claim conspiracy when scientists endeavor to fix the flaw or fill the gap. If the scientists persist in their work, on the assumption that a solution will be found, they are again charged with conspiracy. In fact, the occasions when an entire scientific theory is overthrown by a negative finding are few and far between. This is especially true in fields depending on statistical modeling of complex phenomena for which there are often multiple models that are roughly equally good (or bad), and the choice of a data set and decisions about data-set filtering are often critical. The more important test of a research program is whether progress is being made over a period of time and whether better progress could be made with an alternative approach. Progress can be measured by the accumulation of a solid, verifiable body of knowledge with a very high probability of being correct (Franklin 2009).

Climate Change Conspiracy

The conspiracy meme has been especially prominent in the debate about global warming. When the Intergovernmental Panel on Climate Change published its report in 1996, an eminent retired physicist, Frederick Seitz (1996), accused it of a “major deception on global warming” on the op-ed pages of the Wall Street Journal. Seitz did not offer a scientific argument that the report's conclusions were wrong. Instead, he attacked the committee's procedure in editing its document, accusing the editors of violating their own rules by rewording and rearranging parts of the text to obscure the views of skeptical scientists. This seemingly obscure point about the editing of a UN technical document proved remarkably effective in providing a rallying point for opponents of the report's conclusions.

A careful review of the incident (Lahsen 1999) concluded that the editors did not violate any of their own rules and that the editorial changes were reasonable. Editors, after all, do edit texts, all the more so when the texts are written by a committee. The skeptical arguments were not deleted from the report, but they were repositioned and rephrased, perhaps giving them less emphasis than Seitz thought they deserved. But the conspiracy meme was successful in shifting much of the public debate from the substance of the issue to criticism of personalities, procedures, and motivations. The climate scientists felt attacked and apparently began to think of themselves more as activists under siege than as neutral scientists. In 2009, computer hackers released private e-mails apparently showing that some climate scientists had pressured editors not to publish papers by skeptics and that the climate scientists had looked for ways to present their data to reinforce their advocacy views (Revkin 2009; Hayward 2009; Broder 2010).

Climate science is heavily dependent on complex statistical models based on limited data, so it is not surprising that models based on different assumptions give differing results (Schmidt and Amman 2005). In presenting their data, some scientists were too quick to smooth trends into a “hockey stick” model that fit with their advocacy concerns. Several different groups of well-qualified specialists have now been over the data carefully, and the result is a less linear “hockey stick,” with a rise in temperature during a medieval warm period and a drop during a little ice age. But the sharp increase in warming in the twentieth century, which is the main point of the analysis, is still there (“Hockey stick controversy” 2010; Brumfiel 2006).

This is not the place to review the substance of the issue, which has already been debated extensively in this journal. An encouraging thing, however, is that despite the bitterness is the debate about scientists' behavior, there is considerable consensus on the issue of global warming itself. One of the responsible critics, for example, frankly states that “climate change is a genuine phenomenon, and there is a nontrivial risk of major consequences in the future” (Hayward 2009). But there is no consensus on how high the risk is, how quickly it is likely to materialize, or the costs and benefits of strategies needed to counter it.

The less responsible critics simply dismiss the issue as a hoax and focus exclusively on the peccadilloes of the other side. The climate scientists gave the conspiracy theorists an opening by letting their advocacy color their science, which compromised the legitimacy of their enterprise and, ironically, weakened the political movement itself. This is especially unfortunate because the underlying science is fundamentally correct.

Conspiracy Consequences

Faced with assaults on their professional credibility, scientists may be tempted to retreat from the world of public policy. But allowing the conspiracy theorists to dominate the public debate can have tragic consequences. Fear of science and belief in conspiracies has led British parents to expose their children to life-threatening diseases, the South African health department to reject retroviral treatment for AIDS, and the Zambian government to refuse GM food from the United States in the midst of a famine. Fear of science is not new. Benjamin Franklin was afraid to vaccinate his family against smallpox and regretted it deeply when a son died of the disease in 1736. Parents are making the same mistake today.

Advocacy groups sometimes find it easier to arouse fears of science than to advocate for other goals that may actually be more fundamental to their concerns. The movement against GM foodstuffs in Europe was mobilized largely by anti-capitalist, anti-corporate, and anti-American activists who found it more effective than attacking corporate capitalism directly (Purdue 2000; Schurman 2004). These ideologies have much less support in North America, and efforts to organize against GM food here were much weaker. North Americans have suffered no significant ill effects from the integration of these foods into their diet, a fact that Greenpeace and other advocacy groups studiously ignore. One suspects that if GM seeds had been invented by a socialist government, these advocacy groups would have heralded them as a great victory in the war against hunger.

Public policy requires reaching consensus to make decisions, even though some uncertainty usually remains. If scientists cannot do this, surely it is too much to expect politicians or journalists to do it. Efforts to define a consensus are vulnerable to attacks by conspiracy theorists who portray a consensus as a mechanism for suppressing dissent and debate. But there will always be dissenters, and at a certain point arguing with them becomes unproductive. In 1870, Alfred Russell Wallace allowed himself to be drawn into an extended conflict with Flat Earth theorist John Hampden, editor of the Truth-Seeker's Oracle and Scriptural Science Review. Their dispute over whether the Earth is round involved measuring the curvature of the water on the Old Bedford Canal in England. There was a public wager, which Wallace won, followed by a lawsuit when Hampden refused to pay, a threat against Wallace's life, and a prison term for Hampden. Hampden and his followers were never convinced the Earth is not flat, and belief in the “round Earth conspiracy” apparently persists to this day (Garwood 2008; O'Neill 2008).

Scientists will never reach a consensus with Flat Earthers or with those who believe the Earth was created in 4004 bce. Nor do they need to. The best that science can provide is a clearly specified degree of consensus among scientists who base their conclusions on empirical data. Efforts to reach consensus on important questions have been discouraged due to the influence of philosophers of science who emphasize conflicting research programs, paradigm shifts, and scientific revolutions (Franklin 2009; Stove 1982). Although these events do occur in the history of science, they are exceptional. Most sciences, most of the time, progress with an orderly, gradual accumulation of knowledge that is recognized and accepted by specialists in the field. Opposition rooted in religious or ideological concerns is acceptable as part of the democratic political process, but it need not prevent scientists from reaching a consensus when one is justified.

Peer Review

The peer review process in scientific journals plays a central role in determining which research findings deserve to be incorporated in the scientific consensus on an issue. As such, this process is a target for conspiracy theorists. Peer reviewers are usually anonymous, which suggests they may have something to hide. Although authors' names are usually removed from studies to be reviewed, reviewers are specialists in the same field and can often guess who the authors are. Reviewers are not in a good position to detect actual fraud; they can't redo the experiments or the data analysis. And they may reject papers that go against the conventional wisdom or political consensus in their field (Franklin 2009, 205–11). No adequate alternative to peer review has been proposed, but initiatives to make the review process more transparent may help, including making reviewers' comments and the original data sets available on the Internet.

The credibility of peer review has been undermined in the recent dispute over global warming because the reviewers are drawn from a fairly small pool of specialists who are known to have a policy agenda. The appointment of panels of distinguished scientists to review the body of research in the field is an excellent step to rebuilding credibility (Broder 2010). The review panels must have full access to all the data sets, as well as the time and expertise to conduct their own analyses if necessary-which cannot normally be expected of volunteer reviewers for a journal. It is important that these reviewers give qualified specialists an opportunity to present alternative views, as long as these views are based on scientific analysis of appropriate data and not just polemical criticism. No matter how well they do their work, however, these panels are likely to be attacked by conspiracy theorists.

If the blue-ribbon scientific commissions confirm the original research findings, perhaps with only modest caveats, many people will be convinced. But individuals with strong feelings about the issue may resort to cascade logic, suspecting that the review panel is also part of the conspiracy. Cascade logic can easily develop into a generalized distrust of anything that comes from a mainstream or elite source. In the past, social psychological studies found that this kind of generalized belief in conspiracies was most common among people who were discontented with the established institutions and elite groups in their society, believed that conditions were worsening for people like themselves, and believed that authorities did not care about them (Goertzel 1994; Kramer 1998).

The conspiracy meme can convert a dry scientific issue into a human drama in which malefactors can be exposed and denounced. Scientists are not trained in dealing with this kind of debate, and there is no reason to expect them to be especially good at it. If they also have strong feelings about the issues, they may fall into the conspiracy meme themselves. But when scientists succumb to the temptation to “fight fire with fire,” they risk losing their credibility as experts. It may be tempting to exaggerate findings in mass media outlets by using graphics that highlight the most extreme possibilities. This may be effective in the short run, but the public feels deceived when today's newest scare is refuted by tomorrow's press release; their belief in science is diminished. In today's political climate, scientists need to be careful about releasing their findings on controversial issues; they must make sure the findings have been thoroughly reviewed and that the data sets are available for others to analyze.

Political decisions will inevitably reflect economic interests and emotional concerns that conflict with what scientists believe is best. But scientists can be more effective if they avoid using the conspiracy meme and other rhetorical devices and instead clearly separate their scientific work from their political advocacy as citizens.

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I have inquired for most of my adult life about studies that might show that the death penalty is a deterrent, and I have not seen any research that would substantiate that point.

—Attorney General Janet Reno, January 20, 2000

All of the scientifically valid statistical studies—those that examine a period of years, and control for national trends—consistently show that capital punishment is a substantial deterrent.

— Senator Orrin Hatch, October 16, 2002

It happens all too often. Each side in a policy debate quotes studies that support its point of view and denigrates those from the other side. The result is often that research evidence is not taken seriously by either side. This has led some researchers, especially in the social sciences, to throw up their hands in dismay and give up studying controversial topics. But why bother doing social science research at all if it is impossible to obtain accurate and trustworthy information about issues that matter to people?

There are some questions that social scientists should be able to answer. Either executing people cuts the homicide rate or it does not. Or perhaps it does under certain conditions and not others. In any case, the data are readily available and researchers should be able to answer the question. Of course, this would not resolve the ethical issues surrounding the question, but that is another matter.

So who is right, Janet Reno or Orrin Hatch? And why can they not at least agree on what the data show? The problem is that each of them refers to bodies of research using different research methods. Janet Reno’s statement correctly describes the results of studies that compare homicide trends in states and countries that practice capital punishment with those that do not. These studies consistently show that capital punishment has no effect on homicide rates. Orrin Hatch refers to studies that use econometric modeling. He is wrong, however, in stating that these studies all find that capital punishment deters homicide. In fact, some of them find a deterrent effect and some do not.

But this is not a matter of taste. It cannot be that capital punishment deters homicide for comparative researchers but not for econometricians. In fact, the comparative method has produced valid, useful, and consistent findings, while econometrics has failed in this and every similar area of research.

The first of the comparative studies of capital punishment was done by Thorsten Sellin in 1959. Sellin was a sociologist at the University of Pennsylvania and one of the pioneers of scientific criminology. He was a prime mover in setting up the government agencies that collect statistics on crime. His method involved two steps: “First, a comprehensive view of the subject which incorporated historical, sociological, psychological, and legal factors into the analysis in addition to the development of analytical models; and second, the establishment and utilization of statistics in the evaluation of crime” (Toccafundi 1996).

Sellin applied his combination of qualitative and quantitative methods in an exhaustive study of capital punishment in American states. He used every scrap of data that was available, together with his knowledge of the history, economy, and social structure of each state. He compared states to other states and examined changes in states over time. Every comparison he made led him to the “inevitable conclusion . . . that executions have no discernable effect on homicide rates” (Sellin 1959, 34).

Sellin’s work has been replicated time and time again, as new data have become available, and all of the replications have confirmed his finding that capital punishment does not deter homicide (see Bailey and Peterson 1997, and Zimring and Hawkins 1986). These studies are an outstanding example of what statistician David Freedman (1991) calls “shoe leather” social research. The hard work is collecting the best available data, both quantitative and qualitative. Once the statistical data are collected, the analysis consists largely in displaying them in tables, graphs, and charts which are then interpreted in light of qualitative knowledge of the states in question. This research can be understood by people with only modest statistical background. This allows consumers of the research to make their own interpretations, drawing on their qualitative knowledge of the states in question.

Figure 1 is an example of the kind of chart Sellin prepared, using recent data. The graph compares homicide rates per 100,000 population in Texas, New York, and California. From 1982 to 2002, Texas executed 239 prisoners, California ten, and New York none. The trends in homicide statistics are very similar in all three states, all of which follow national trends. These states were chosen arbitrarily, but data for other states are readily available. If you prefer to compare Texas to Oklahoma, Arkansas, or New Mexico, the data are readily available in back issues of the Statistical Abstract of the United States and Uniform Crime Reports. The results will be much the same.

Hundreds of comparisons of this sort have been made, and they consistently show that the death penalty has no effect. There have also been international comparative studies. Archer and Gartner (1984) examined fourteen countries that abolished the death penalty and found that abolition did not cause an increase in homicide rates. This research has been convincing to most criminologists (Radelet and Akers n.d.; Fessenden 2000), which is why Janet Reno was told that there was no valid research linking capital punishment to homicide rates.

The studies that Orrin Hatch referred to use a very different methodology: econometrics, also known as multiple regression modeling, structural equation modeling, or path analysis. This involves constructing complex mathematical models on the assumption that the models mirror what happens in the real world. As I argued in a previous Skeptical Inquirer article (Goertzel 2002), this method has consistently failed to offer reliable and valid results in studies of social problems where the data are very limited. Its most successful use is in making predictions in areas where there is a large flow of data for testing. The econometric literature on capital punishment has been carefully reviewed by several prominent economists and found wanting. There is simply too little data and too many ways to manipulate it. In one careful review, McManus (1985, 417) found that: “there is much uncertainty as to the ‘correct’ empirical model that should be used to draw inferences, and each researcher typically tries dozens, perhaps hundreds, of specifications before selecting one or a few to report. Usually, and understandably, the ones selected for publication are those that make the strongest case for the researcher’s prior hypothesis.”

Models that find deterrence effects of capital punishment often rely on rather bizarre specifications. In a rigorous and comprehensive review Cameron (1994, 214) observed that, “What emerges most strongly from this review is that obtaining a significant deterrent effect of executions seems to depend on adding a set of data with no executions to the time series and including an executing/non-executing dummy in the cross-section analysis . . . there is no clear justification for the latter practice.”

In less technical language, the researchers included a set of years when there were no executions, then introduced a control variable to eliminate the nonexistent variance. The other day upon the stair, they saw some variance that wasn’t there. It wasn’t there again today, thank goodness their model scared it away. Not all the studies rely on this particular maneuver, but they all depend on techniques that demand too much from the available data.

Since there are so many ways to model inadequate data, McManus (1985, 425) was able to show that researchers whose prior beliefs led them to structure their models in different ways would obtain predictable conclusions: “The data analyzed are not sufficiently strong to lead researchers with different prior beliefs to reach a consensus regarding the deterrent effects of capital punishment. Right-winger, rational-maximizer, and eye-for-an-eye researchers will infer that punishment deters would-be murderers, but bleeding-heart and crime-of-passion researchers will infer that there is no significant deterrent effect.”

The Mythical World of Ceteris Paribus

Econometricians inhabit the mythical land of Ceteris Paribus, a place where everything is constant except the variables they choose to write about. Ceteris Paribus has much in common with the mythical world of Flatland in Edwin Abbot’s (1884) classic fairy tale. In Flatland everything moves along straight lines, flat plains, or rectangular boxes. In Flatland, statistical averages become mathematical laws. For example, it is true that, on the average, tall people weigh more than short people. But, in the real world, not every tall person weighs more than a shorter one. In Flatland knowing someone’s height would be enough to tell you their precise weight, because both vary only on a straight line. In Flatland, if you plotted height and weight on a graph with height on one axis and weight on the other, all the points would fall on a straight line.

Of course, econometricians know that they don’t live in Flatland. But the mathematics works much better when they pretend they do. So they adjust the data in one way or another to make it straighter (often by converting it to logarithms). Then they qualify their remarks, saying “capital punishment deters homicide, ceteris paribus.” But when the real-world data diverge greatly from the straight lines of Flatland, this can lead to bizarre results.

Figure 2: Anscombe’s Quartet (by J. Randall Flannigan)

Statistician Francis Anscombe (1973) demonstrated how bizarre the Flatland assumption can be. He plotted four graphs that have become known as Anscombe’s Quartet. Each of the graphs shows the relationship between two variables. The graphs are very different, but for a resident of Flatland they are all the same. If we approximate them with a straight line (following a “linear regression equation”) the lines are all the same (figure 2). Only the first of Anscombe’s four graphs is a reasonable candidate for a linear regression analysis, because a straight line is a reasonable approximation for the underlying pattern.

Figure 3: Executions and murder rates in the United States.

The data on capital punishment and homicide, when plotted in figure 3, look a lot like Anscombe’s fourth quartet. Most of the states had no executions at all. One state, Texas, accounts for forty of the eighty-five executions in the year shown (the patterns for other years are quite similar). An exceptional case or “outlier” of this dimension completely dominates a multiple regression analysis. Any regression study will be primarily a comparison of Texas with everywhere else. Multiple regression is simply inappropriate with this data, no matter how hard the analyst tries to force the data into a linear pattern.

Unfortunately, econometricians continue to use multiple regression on capital punishment data and to generate results that are cited in Congressional hearings. In recent examples, Mocan and Gittings (2001) concluded that each execution decreases the number of homicides by five or six while Dezhbaksh, Rubin, and Shepherd (2002) argued that each execution deters eighteen murders. Cloninger and Marchesini (2001) published a study finding that the Texas moratorium from March 1996 to April 1997 increased homicide rates, even though no increase can be seen in the graph (figure 1). The moratorium simply increased homicide in comparison to what their econometric model said it would have otherwise been. Of all the econometric myths, the wildest is this: We know what would have been.

Cloninger and Marchesini concede that “studies such as the present one that rely on inductive statistical analysis cannot prove a given hypothesis correct.” However, they argue that when a large number of such studies give the same result, this provides “robust evidence” which “causes any neutral observer pause.” But if McManus is correct that econometricians are likely to specify models to fit their preconceptions, then if many of them reach the same conclusion it may just mean that they have the same bias. Actually, there are a variety of biases among econometricians, which is why there are almost as many on one side as on the other of this issue. In response to Ehrlich’s (1975) initial econometric study, other econometricians using the same data included Yunker (1976), who found a stronger deterrent effect than Ehrlich, and Cloninger (1977), who supported his findings. But Bowers and Pierce (1975), Passel and Taylor (1977), and Hoenack and Weiler (1980) found no deterrence at all.

Econometricians often dismiss the kind of comparative research that Thorsten Sellin did as crude and unsophisticated when compared to their use of complex mathematical formulas. But mathematical complexity does not make for good social science. The goal of multiple regression is to convert messy sociological realities into math problems that can be resolved with the certainty of mathematical proof. Econometricians believe they can control for the myriad variables that affect homicide rates, just as a chemist eliminates impurities to see how two chemicals interact in their pure form. But they cannot convert the real world into a Flatland, so they use statistical adjustments to compensate. With these adjustments, they claim to answer the Ceteris Paribus question: If everything else were equal, what would the relationship between capital punishment and homicide be?

It would be handy for social scientists if we lived in a Flatland where everything else was equal and questions could be answered with a few calculations. But multivariate statistical analysis does not answer real-world questions such as, “does Texas, with a high execution rate, have a lower homicide rate than similar states?” or “did the homicide rate go down when Texas began executing people, compared to trends in other states that did not?” Instead, it answers the question, “If we use the latest, most sophisticated statistical methods to control for extraneous variables, can we say that the death penalty deters homicide rates other things being equal?” After decades of effort by many diligent researchers, we now know the answer to this question: There are many ways to adjust things statistically, and the answer will depend on which one is chosen. We also know that of the many possible ways to specify a regression model, each researcher is likely to prefer one that will give results consistent with his or her predispositions.

It is time to abandon the illusion that mathematics can convert the real world into the mythical land of Ceteris Paribus. Social science can provide valid and reliable results with methods that present the data with as little statistical manipulation as possible and interpret it in light of the best qualitative information available. The value of this research is shown by its success in demonstrating that capital punishment has not deterred homicide.

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