Hiring Salespeople You Only Dreamed About (Part 2)

from the A. C. Clarke foundation

I ended Part 1 with Arthur C. Clarke’s third law, “Any sufficiently advanced (hiring) technology is indistinguishable from magic.” It’s a play on words, but describes the same reaction I get from almost everyone unfamiliar with the best-practices outlined in 1978 in the Uniform Guidelines on Employee Selection Procedures.

Typical question, “How do I know this really works?”

Typical reply, “You tell me. If instead of working from a job description and doing a casual interview, I thoroughly define job skills, then use a variety of hard-to-fake accurate tools that evaluate whether a candidate has those specific skills, what do you think?”


After assessing hundreds of salespeople and sales managers (i.e., observing a full complement of their sales and management skills), I began to recognize some specific trends (I’m a slow learner).

Although they vary somewhat with type and position, they break down into just four general groups: 1) not being smart enough (or, conversely, being too smart); 2) being poorly organized and unable to focus (or conversely, being too nit-icky); 3) having insufficient people skills to manage, coach, sell, service, or work in a team; and, 4) having the wrong (or no) motivations to perform. (In some jobs physical skills are also important, but I’ll skip those for now).

Did you notice these factors have less to do with the environment or how they are treated than with the individual? And, for the “give ’em a chance crowd,” they are very hard, if not impossible, to develop. That’s why these skills are so important to measure pre hire. If someone wants to take a chance on a salesperson who is dull, disorganized, has poor interpersonal skills, or is wrongly motivated, then be my guest … just do it with your own money, not mine.

How Do These Elements Play Out on the Job?

Here’s a reality check. First, have you ever felt “you know ’em when you see ’em”? If yes, then you are not alone. Now be honest, are the vast majority of the people who you hire successful salespeople? If your answer is, “ummm, no,” then I think we can all agree the “know-em-when you see ’em” technique does not work very well. You were snookered by an empty suit (just like everyone else). Now let’s move on.

Not being smart enough is usually seen early (i.e., during training when the candidate has trouble understanding the product or service) or it can be seen late (i.e., when the candidate cannot develop an account penetration strategy, ask the right questions to discover a problem that needs a solution, or cannot keep up with changing technology). Here is an example: suppose on a scale of 1 to 10, where 5 is average, your product or service needs someone with a mental horsepower level of 7. Do you think a person with a 5 could do the job? How about someone with a 10? Not matching a candidate’s mental horsepower to the job is a sure path to disaster.

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What about personal organization? Anyone who has ever worked with salespeople knows keeping records and following through on commitments is not one of their natural strengths. But at what level does it interfere with performance: a 3, 5, or a 7? It all depends on the complexity and nature of the job. Aim too high on the organizational scale and you get analysis-paralysis. Aim too low and you get customer service and delivery issues. I’m sure you have seen as many examples as I have.

Then there is the whole problem of interpersonal skills. Do you think they are soft and unimportant? If a big tree falls in the woods, does it crunch slow-moving critters living underneath? Being an effective salesperson requires exceptional interpersonal skills, starting with trust builders; and, they continue throughout the sales relationship as trust maintainers. Have you ever read studies that show customer loyalty tends to increase after a problem is successfully solved? Doesn’t it make sense to you that solving the problem and maintaining trust had something to do with that? Here is some free advice: never hire someone who claims they can sell anything to anyone, unless you enjoy dealing with buyer’s remorse.

Last of all, we have the motivational pieces. In my experience, effective salespeople are driven to compete and succeed. It’s as if they have a leaky self-validation bucket that always needs re-filling. Are candidates going to tell you they don’t have the right motivations? Sure, right after you invest time, training, coaching, materials, price concessions, and let them burn through dozens of clients and prospects. Of course, if you hire someone with too much motivation, you get Attila the Hun (or if it’s a female, Attila the Honey). If you hire too little, you get soggy milquetoast. This goes for providing customer service and a host of other motivational factors as well. What about hiring a highly motivated person with inadequate selling skills? Good luck with that train wreck!

Hopefully, these examples have convinced you “know ‘em when you see ‘em” decisions might feel warm and comfy because they are supported by the balance of consequences (remember, P-I-C almost always trumps O-U-D; see part 1 for an explanation of these acronyms).

Salespeople are great at selling themselves, but only occasionally deliver the goods. As you can imagine, measuring sales skills takes more than a casual interview, scrutinizing a W-2, or selling a wastebasket. It takes a combination of job analyses and hard-to-fake tests, motivations, and simulations.


20 Comments on “Hiring Salespeople You Only Dreamed About (Part 2)

  1. WW, how many salespeople have you actually hired and developed in your career?

    I’m no expert, but after 15 years in business and a few dozen hires, I can say this: you can’t tell by looking, and you can’t just measure IQ, organization, or EQ skills by themselves.

    What I have seen is that people change (and are changed) by the experience, and a lot of it is timing and “karma”.

    Check this incredibily interesting article:


  2. Be nice, Martin. I’ve hired dozens, trained hundreds, ran a sales department for years, set up two sales training departments for large financial services firms, and been a people manager since college. Ok?

    You really have to stop believing opinion articles. In all the times I have been interviewed, I have yet to meet a writer who wants to do more than satisfy their editor and meet a deadline. I suppose karma might play into OJP, but most professionals I know think performance starts with having the right job skills.

    BTW. I often get the most negative feedback from people who had a bad experience with “tests” in the past; but, since even a simple interview or resume-scan is a test, don’t you think it should be done accurately?

    The only other alternative is to hire everyone and let the job shake them out. As a recruiter, do you submit/place/hire everyone who sends you a resume regardless of the position? Or do you first try to “test” their qualifications and experience by asking a few questions?

  3. Hey that’s why I was asking WW!

    And only a fool would just try to slot anybody in any position- or try to argue that position.

    Likewise; in mission-critical, life and death jobs, being too picky is no vice, and the more fully and practically those jobs can be simulated, (e.g.airline flying, surgery, electronic warfare) the better off we all are. I could not be more supportive of those kinds of pre-hire and pre-qualifaction efforts.

    That said, I have seen some who would appear to be totally disorganized introverts perform with distinction in certain sales roles. Sales performance, as much or more than almost any other role, turns on leadership, timing, and externalities beyond the control of the salesperson.

    That item on collective intellegence (IMHO) is not a mere opinion piece – it’s an operative effect in the real-world that must be accounted for when doing that which you suggest must be done; an application of all pertinent information prior to making a personnel decision.

  4. @Martin: I quickly went through the article. and found it quite interesting. ISTM that certain types of problems/tasks might best be performed by the mentioned groups, while taking care to maximize the number of effective group members (high-empathy individuals). Other types of tasks/problems might best be handled by people who should be excluded from those groups (low empathy Aspies and domineering A-types).



  5. Keith I could probably make a decent living just screening for Asperger’s – at least in males anyway. I know it when I see it, usually within a few minutes. With females, I have a much harder time seeing it, prolly because of the wiring differences between the genders, esp. on the social aspects.

    While they may be technically low on empathy, my experience has been that people with high-function ASD’s are some of the nicest, best-meaning folks you will ever meet- not a mean bone in their bodies- almost as if the lack of subliminal empathy fosters a hypertrophy of effort to try to understand others.

    It’s also really odd and interesting just how often aspies and bullies seem to attract each other and form a symbiotic relationship- I have seen it again and again, in some cases where the bully will actually defend the aspie they are bullying from other bullies!

    Unlike the devotees of determinism, I do truly believe that groups act in ways not predicted by individual assessment, in just that type of odd dynamic that you would not count on or be able to quantify without the group context.

  6. While it’s true that group dynamics can have either a positive or negative effect on performance, it’s still more important to ensure each player has the right skills to do the job. Articles like the one cited above encourage people to take their eye off the individual skills ball by focusing on hroup-level trends and not on individual people.

    For example, a recent research article showed that people within certain cities tend to have stronger left-wing attitudes. But, while it might be fun to know cities are different, the study does not tell us anything about a specific individual. In other words, just because SF has the highest left-wing score in the US, we cannot claim everyone in SF is a lefty. Furthermore, just because someone is a lefty, it does not mean they will enjoy SF.

    There is too much pop psychology floating around. If people associated with recruiting know how to distinguish useful from useless information, they can make some serious mistakes.

  7. WW,

    MIT has a somewhat sound reputation for doing serious work, and I don’t advocate just winging it on feelings and guesses- only that individual assessment is bound to be incomplete without group context FOR THOSE ROLES that are highly creative, leadership driven, and team-oriented.

    And labels will let you down too: what do you call a person who wants a top marginal tax rate of 80%, is a strong advocate of capital punishment, wants public-sector unions outlawed, is pro-gun rights, believes that affirmative action is a giant scam and moral outrage, and thinks the welfare system is vastly underfunded ?

    Probably not a “lefty”, regardless of locale.

  8. Ok…this is a good point to pursue. First, suppose a respected investigator successfully argues individual performance varies with the group to which he or she belongs. (Ring the bell!)

    However, we know from experience that work groups change from time to time (or from project to project); group members in the same workgroup change from time to time; and, workgroup managers (the biggest source of job dissatisfaction) change from time to time. How are you going to use this information to accurately determine whether a new employee is skill-qualified?

  9. WW that raises a whole other kettle of fish. Skills come and go- both personally and economically, which is why skills are only a third of the KSA formulation, which itself is suspect if what we are starting to really understand about groups is true.

    How are you going to test for group fit is a very good question: those who answer it well are going to prosper in the future assessment businesss. It may be simulation, instrument based, structured group interactions/experiences, or even more bizzare: actually granting selection/hiring authority to affected groups.

    My gut and my experience tell me that a single player can make or break a whole team, and that small group dynamics are among the most powerful motivators. There are many employees who may not give a crap about their company, their job, etc. but they care greatly about the opinions/respect of their immediate co-workers: the out-of- fashion term for that incredibily important dynamic is “morale”.

    George C. Marshall, one of the greatest but least known American leaders, said that “morale is the state of mind. It is steadfastness and courage and hope. It is confidence and zeal and loyalty. It is élan, esprit de corps and determination”

    WW, that “state of mind” does not just come from nowhere, and it does not have all that much to do with hard skills. Give me a skilled but not great person who lifts a group any day of the week v. very top skills in a person who supresses a group’s morale.

    The great generals (think Belichick) dont give a rat’s ass about marginal skill differences- as long as you are in the ballpark of course.

    All they care about is how you work with your team and how fully you buy into the program, because when that happens, skill levels appear to improve as if by magic. That’s why he takes undrafted nobody’s and makes stars out of them, and that’s why he can beat you with his team and then turn around and beat you with your team too.

    Recognizing skills is not difficult, but not game-changing either, and that’s why assessment needs to move past that entry-level and get to the stuff that DOES change the game.

  10. I enjoy a good argument, but this is going nowhere and (worse) is totally misleading. If you really want to be a selection and placement expert, buy the Handbook of Employee Selection (Farr and Tippins, eds), study it from cover to cover, then tell us all why you think it’s limited to entry-level?

    While you are at it, you might also find the 1978 Uniform Guidelines an interesting read…it talks all about all that “unimportant” entry-level stuff and why the OFCCP and EEOC hold organizations accountable if they don’t follow it.

  11. WW it’s going nowhere because you dont want to address the subjects of collective intellgence /leadership and what they really mean to employee selection, nor will you start from the premise that I accept the undoubted efficacy of validated job analysis/selection criteria v. just winging it, which of course is unacceptable when hiring at scale.

    To unpack your last comment: I make no pretension of expertise in the field. I have been commenting on ERE for a decade, including many, many references to the Uniform Guidelines. Through the great efforts of dozens of people that I played a role in hiring, we have managed to sell maybe $50,000,000 worth of software and services, a substantial portion of which is directly related to avoiding trouble with the OFCCP and EEOC. I probably knows some things that you don’t on those subjects, but you are a PhD and I am an auto-didact college dropout, so mileage may vary.

    http://cci.mit.edu/ is the home page of the center for collective intelligence program. I’m confident that it’s staffed by some big brains who know a lot more than either of us on the subject.

    I remain convinced that assessing group dynamics will the norm in 20 years and will be seen as a critical pre-hire activity, while today it’s not even on the radar.

  12. @WW,@MS: I’ve lost the points each of you were trying to make in the mutual sniping. Please concisely restate them.

    Thank You,

    Keith “Not Always Fanning the Flames” Halperin

  13. Just found something interesting re: this:

    A study done by Peter Schulman at the Wharton School, published in the Journal of Selling and Sales Management, looked to determine the affects of applying learned optimism in business. After measuring the optimism levels of an insurance sales force, it was determined that the optimistic sales people sold 35 percent more, and identified pessimists were two times more likely to quit in the first year than optimists. As a result of his studies, he recommends testing sales job candidates for optimism levels to fit them to appropriate positions, training employees in learned optimism techniques, and designing an organization overall to have attainable goals set and good support from management.

    Schulman, Peter. Applying Learned Optimism to Increase Sales Productivity. Journal of Personal Selling and Sales Management. Volume XIX, Number 1, Winter 1999. Pages 31–37.[2]



  14. @ Everybody:
    Dr. Williams pointed out to me off-forum that the study I cited was shoddy.

    But there’s more:

    Annals of Science
    The Truth Wears OffIs there something wrong with the scientific method?by Jonah Lehrer
    December 13, 2010 .Many results that are rigorously proved and accepted start shrinking in later studies.

    SharePrintE-MailSingle Page
    Scientific Experiments;Decline Effect;Replicability;Scientists;Statistics;Jonathan Schooler;Scientific TheoriesOn September 18, 2007, a few dozen neuroscientists, psychiatrists, and drug-company executives gathered in a hotel conference room in Brussels to hear some startling news. It had to do with a class of drugs known as atypical or second-generation antipsychotics, which came on the market in the early nineties. The drugs, sold under brand names such as Abilify, Seroquel, and Zyprexa, had been tested on schizophrenics in several large clinical trials, all of which had demonstrated a dramatic decrease in the subjects’ psychiatric symptoms. As a result, second-generation antipsychotics had become one of the fastest-growing and most profitable pharmaceutical classes. By 2001, Eli Lilly’s Zyprexa was generating more revenue than Prozac. It remains the company’s top-selling drug.

    But the data presented at the Brussels meeting made it clear that something strange was happening: the therapeutic power of the drugs appeared to be steadily waning. A recent study showed an effect that was less than half of that documented in the first trials, in the early nineteen-nineties. Many researchers began to argue that the expensive pharmaceuticals weren’t any better than first-generation antipsychotics, which have been in use since the fifties. “In fact, sometimes they now look even worse,” John Davis, a professor of psychiatry at the University of Illinois at Chicago, told me.

    Before the effectiveness of a drug can be confirmed, it must be tested and tested again. Different scientists in different labs need to repeat the protocols and publish their results. The test of replicability, as it’s known, is the foundation of modern research. Replicability is how the community enforces itself. It’s a safeguard for the creep of subjectivity. Most of the time, scientists know what results they want, and that can influence the results they get. The premise of replicability is that the scientific community can correct for these flaws.

    But now all sorts of well-established, multiply confirmed findings have started to look increasingly uncertain. It’s as if our facts were losing their truth: claims that have been enshrined in textbooks are suddenly unprovable. This phenomenon doesn’t yet have an official name, but it’s occurring across a wide range of fields, from psychology to ecology. In the field of medicine, the phenomenon seems extremely widespread, affecting not only antipsychotics but also therapies ranging from cardiac stents to Vitamin E and antidepressants: Davis has a forthcoming analysis demonstrating that the efficacy of antidepressants has gone down as much as threefold in recent decades.

    For many scientists, the effect is especially troubling because of what it exposes about the scientific process. If replication is what separates the rigor of science from the squishiness of pseudoscience, where do we put all these rigorously validated findings that can no longer be proved? Which results should we believe? Francis Bacon, the early-modern philosopher and pioneer of the scientific method, once declared that experiments were essential, because they allowed us to “put nature to the question.” But it appears that nature often gives us different answers.

    from the issuecartoon banke-mail this.Jonathan Schooler was a young graduate student at the University of Washington in the nineteen-eighties when he discovered a surprising new fact about language and memory. At the time, it was widely believed that the act of describing our memories improved them. But, in a series of clever experiments, Schooler demonstrated that subjects shown a face and asked to describe it were much less likely to recognize the face when shown it later than those who had simply looked at it. Schooler called the phenomenon “verbal overshadowing.”

    The study turned him into an academic star. Since its initial publication, in 1990, it has been cited more than four hundred times. Before long, Schooler had extended the model to a variety of other tasks, such as remembering the taste of a wine, identifying the best strawberry jam, and solving difficult creative puzzles. In each instance, asking people to put their perceptions into words led to dramatic decreases in performance.

    But while Schooler was publishing these results in highly reputable journals, a secret worry gnawed at him: it was proving difficult to replicate his earlier findings. “I’d often still see an effect, but the effect just wouldn’t be as strong,” he told me. “It was as if verbal overshadowing, my big new idea, was getting weaker.” At first, he assumed that he’d made an error in experimental design or a statistical miscalculation. But he couldn’t find anything wrong with his research. He then concluded that his initial batch of research subjects must have been unusually susceptible to verbal overshadowing. (John Davis, similarly, has speculated that part of the drop-off in the effectiveness of antipsychotics can be attributed to using subjects who suffer from milder forms of psychosis which are less likely to show dramatic improvement.) “It wasn’t a very satisfying explanation,” Schooler says. “One of my mentors told me that my real mistake was trying to replicate my work. He told me doing that was just setting myself up for disappointment.”

    Schooler tried to put the problem out of his mind; his colleagues assured him that such things happened all the time. Over the next few years, he found new research questions, got married and had kids. But his replication problem kept on getting worse. His first attempt at replicating the 1990 study, in 1995, resulted in an effect that was thirty per cent smaller. The next year, the size of the effect shrank another thirty per cent. When other labs repeated Schooler’s experiments, they got a similar spread of data, with a distinct downward trend. “This was profoundly frustrating,” he says. “It was as if nature gave me this great result and then tried to take it back.” In private, Schooler began referring to the problem as “cosmic habituation,” by analogy to the decrease in response that occurs when individuals habituate to particular stimuli. “Habituation is why you don’t notice the stuff that’s always there,” Schooler says. “It’s an inevitable process of adjustment, a ratcheting down of excitement. I started joking that it was like the cosmos was habituating to my ideas. I took it very personally.”

    Schooler is now a tenured professor at the University of California at Santa Barbara. He has curly black hair, pale-green eyes, and the relaxed demeanor of someone who lives five minutes away from his favorite beach. When he speaks, he tends to get distracted by his own digressions. He might begin with a point about memory, which reminds him of a favorite William James quote, which inspires a long soliloquy on the importance of introspection. Before long, we’re looking at pictures from Burning Man on his iPhone, which leads us back to the fragile nature of memory.

    Although verbal overshadowing remains a widely accepted theory—it’s often invoked in the context of eyewitness testimony, for instance—Schooler is still a little peeved at the cosmos. “I know I should just move on already,” he says. “I really should stop talking about this. But I can’t.” That’s because he is convinced that he has stumbled on a serious problem, one that afflicts many of the most exciting new ideas in psychology.

    One of the first demonstrations of this mysterious phenomenon came in the early nineteen-thirties. Joseph Banks Rhine, a psychologist at Duke, had developed an interest in the possibility of extrasensory perception, or E.S.P. Rhine devised an experiment featuring Zener cards, a special deck of twenty-five cards printed with one of five different symbols: a card was drawn from the deck and the subject was asked to guess the symbol. Most of Rhine’s subjects guessed about twenty per cent of the cards correctly, as you’d expect, but an undergraduate named Adam Linzmayer averaged nearly fifty per cent during his initial sessions, and pulled off several uncanny streaks, such as guessing nine cards in a row. The odds of this happening by chance are about one in two million. Linzmayer did it three times.

    Rhine documented these stunning results in his notebook and prepared several papers for publication. But then, just as he began to believe in the possibility of extrasensory perception, the student lost his spooky talent. Between 1931 and 1933, Linzmayer guessed at the identity of another several thousand cards, but his success rate was now barely above chance. Rhine was forced to conclude that the student’s “extra-sensory perception ability has gone through a marked decline.” And Linzmayer wasn’t the only subject to experience such a drop-off: in nearly every case in which Rhine and others documented E.S.P. the effect dramatically diminished over time. Rhine called this trend the “decline effect.”

    Schooler was fascinated by Rhine’s experimental struggles. Here was a scientist who had repeatedly documented the decline of his data; he seemed to have a talent for finding results that fell apart. In 2004, Schooler embarked on an ironic imitation of Rhine’s research: he tried to replicate this failure to replicate. In homage to Rhine’s interests, he decided to test for a parapsychological phenomenon known as precognition. The experiment itself was straightforward: he flashed a set of images to a subject and asked him or her to identify each one. Most of the time, the response was negative—the images were displayed too quickly to register. Then Schooler randomly selected half of the images to be shown again. What he wanted to know was whether the images that got a second showing were more likely to have been identified the first time around. Could subsequent exposure have somehow influenced the initial results? Could the effect become the cause?

    The craziness of the hypothesis was the point: Schooler knows that precognition lacks a scientific explanation. But he wasn’t testing extrasensory powers; he was testing the decline effect. “At first, the data looked amazing, just as we’d expected,” Schooler says. “I couldn’t believe the amount of precognition we were finding. But then, as we kept on running subjects, the effect size”—a standard statistical measure—“kept on getting smaller and smaller.” The scientists eventually tested more than two thousand undergraduates. “In the end, our results looked just like Rhine’s,” Schooler said. “We found this strong paranormal effect, but it disappeared on us.”

    The most likely explanation for the decline is an obvious one: regression to the mean. As the experiment is repeated, that is, an early statistical fluke gets cancelled out. The extrasensory powers of Schooler’s subjects didn’t decline—they were simply an illusion that vanished over time. And yet Schooler has noticed that many of the data sets that end up declining seem statistically solid—that is, they contain enough data that any regression to the mean shouldn’t be dramatic. “These are the results that pass all the tests,” he says. “The odds of them being random are typically quite remote, like one in a million. This means that the decline effect should almost never happen. But it happens all the time! Hell, it’s happened to me multiple times.” And this is why Schooler believes that the decline effect deserves more attention: its ubiquity seems to violate the laws of statistics. “Whenever I start talking about this, scientists get very nervous,” he says. “But I still want to know what happened to my results. Like most scientists, I assumed that it would get easier to document my effect over time. I’d get better at doing the experiments, at zeroing in on the conditions that produce verbal overshadowing. So why did the opposite happen? I’m convinced that we can use the tools of science to figure this out. First, though, we have to admit that we’ve got a problem.”

    In 1991, the Danish zoologist Anders Møller, at Uppsala University, in Sweden, made a remarkable discovery about sex, barn swallows, and symmetry. It had long been known that the asymmetrical appearance of a creature was directly linked to the amount of mutation in its genome, so that more mutations led to more “fluctuating asymmetry.” (An easy way to measure asymmetry in humans is to compare the length of the fingers on each hand.) What Møller discovered is that female barn swallows were far more likely to mate with male birds that had long, symmetrical feathers. This suggested that the picky females were using symmetry as a proxy for the quality of male genes. Møller’s paper, which was published in Nature, set off a frenzy of research. Here was an easily measured, widely applicable indicator of genetic quality, and females could be shown to gravitate toward it. Aesthetics was really about genetics.

    In the three years following, there were ten independent tests of the role of fluctuating asymmetry in sexual selection, and nine of them found a relationship between symmetry and male reproductive success. It didn’t matter if scientists were looking at the hairs on fruit flies or replicating the swallow studies—females seemed to prefer males with mirrored halves. Before long, the theory was applied to humans. Researchers found, for instance, that women preferred the smell of symmetrical men, but only during the fertile phase of the menstrual cycle. Other studies claimed that females had more orgasms when their partners were symmetrical, while a paper by anthropologists at Rutgers analyzed forty Jamaican dance routines and discovered that symmetrical men were consistently rated as better dancers.

    Then the theory started to fall apart. In 1994, there were fourteen published tests of symmetry and sexual selection, and only eight found a correlation. In 1995, there were eight papers on the subject, and only four got a positive result. By 1998, when there were twelve additional investigations of fluctuating asymmetry, only a third of them confirmed the theory. Worse still, even the studies that yielded some positive result showed a steadily declining effect size. Between 1992 and 1997, the average effect size shrank by eighty per cent.

    And it’s not just fluctuating asymmetry. In 2001, Michael Jennions, a biologist at the Australian National University, set out to analyze “temporal trends” across a wide range of subjects in ecology and evolutionary biology. He looked at hundreds of papers and forty-four meta-analyses (that is, statistical syntheses of related studies), and discovered a consistent decline effect over time, as many of the theories seemed to fade into irrelevance. In fact, even when numerous variables were controlled for—Jennions knew, for instance, that the same author might publish several critical papers, which could distort his analysis—there was still a significant decrease in the validity of the hypothesis, often within a year of publication. Jennions admits that his findings are troubling, but expresses a reluctance to talk about them publicly. “This is a very sensitive issue for scientists,” he says. “You know, we’re supposed to be dealing with hard facts, the stuff that’s supposed to stand the test of time. But when you see these trends you become a little more skeptical of things.”

    What happened? Leigh Simmons, a biologist at the University of Western Australia, suggested one explanation when he told me about his initial enthusiasm for the theory: “I was really excited by fluctuating asymmetry. The early studies made the effect look very robust.” He decided to conduct a few experiments of his own, investigating symmetry in male horned beetles. “Unfortunately, I couldn’t find the effect,” he said. “But the worst part was that when I submitted these null results I had difficulty getting them published. The journals only wanted confirming data. It was too exciting an idea to disprove, at least back then.” For Simmons, the steep rise and slow fall of fluctuating asymmetry is a clear example of a scientific paradigm, one of those intellectual fads that both guide and constrain research: after a new paradigm is proposed, the peer-review process is tilted toward positive results. But then, after a few years, the academic incentives shift—the paradigm has become entrenched—so that the most notable results are now those that disprove the theory.

    Jennions, similarly, argues that the decline effect is largely a product of publication bias, or the tendency of scientists and scientific journals to prefer positive data over null results, which is what happens when no effect is found. The bias was first identified by the statistician Theodore Sterling, in 1959, after he noticed that ninety-seven per cent of all published psychological studies with statistically significant data found the effect they were looking for. A “significant” result is defined as any data point that would be produced by chance less than five per cent of the time. This ubiquitous test was invented in 1922 by the English mathematician Ronald Fisher, who picked five per cent as the boundary line, somewhat arbitrarily, because it made pencil and slide-rule calculations easier. Sterling saw that if ninety-seven per cent of psychology studies were proving their hypotheses, either psychologists were extraordinarily lucky or they published only the outcomes of successful experiments. In recent years, publication bias has mostly been seen as a problem for clinical trials, since pharmaceutical companies are less interested in publishing results that aren’t favorable. But it’s becoming increasingly clear that publication bias also produces major distortions in fields without large corporate incentives, such as psychology and ecology.

    While publication bias almost certainly plays a role in the decline effect, it remains an incomplete explanation. For one thing, it fails to account for the initial prevalence of positive results among studies that never even get submitted to journals. It also fails to explain the experience of people like Schooler, who have been unable to replicate their initial data despite their best efforts. Richard Palmer, a biologist at the University of Alberta, who has studied the problems surrounding fluctuating asymmetry, suspects that an equally significant issue is the selective reporting of results—the data that scientists choose to document in the first place. Palmer’s most convincing evidence relies on a statistical tool known as a funnel graph. When a large number of studies have been done on a single subject, the data should follow a pattern: studies with a large sample size should all cluster around a common value—the true result—whereas those with a smaller sample size should exhibit a random scattering, since they’re subject to greater sampling error. This pattern gives the graph its name, since the distribution resembles a funnel.

    The funnel graph visually captures the distortions of selective reporting. For instance, after Palmer plotted every study of fluctuating asymmetry, he noticed that the distribution of results with smaller sample sizes wasn’t random at all but instead skewed heavily toward positive results. Palmer has since documented a similar problem in several other contested subject areas. “Once I realized that selective reporting is everywhere in science, I got quite depressed,” Palmer told me. “As a researcher, you’re always aware that there might be some nonrandom patterns, but I had no idea how widespread it is.” In a recent review article, Palmer summarized the impact of selective reporting on his field: “We cannot escape the troubling conclusion that some—perhaps many—cherished generalities are at best exaggerated in their biological significance and at worst a collective illusion nurtured by strong a-priori beliefs often repeated.”

    Palmer emphasizes that selective reporting is not the same as scientific fraud. Rather, the problem seems to be one of subtle omissions and unconscious misperceptions, as researchers struggle to make sense of their results. Stephen Jay Gould referred to this as the “shoehorning” process. “A lot of scientific measurement is really hard,” Simmons told me. “If you’re talking about fluctuating asymmetry, then it’s a matter of minuscule differences between the right and left sides of an animal. It’s millimetres of a tail feather. And so maybe a researcher knows that he’s measuring a good male”—an animal that has successfully mated—“and he knows that it’s supposed to be symmetrical. Well, that act of measurement is going to be vulnerable to all sorts of perception biases. That’s not a cynical statement. That’s just the way human beings work.”

    One of the classic examples of selective reporting concerns the testing of acupuncture in different countries. While acupuncture is widely accepted as a medical treatment in various Asian countries, its use is much more contested in the West. These cultural differences have profoundly influenced the results of clinical trials. Between 1966 and 1995, there were forty-seven studies of acupuncture in China, Taiwan, and Japan, and every single trial concluded that acupuncture was an effective treatment. During the same period, there were ninety-four clinical trials of acupuncture in the United States, Sweden, and the U.K., and only fifty-six per cent of these studies found any therapeutic benefits. As Palmer notes, this wide discrepancy suggests that scientists find ways to confirm their preferred hypothesis, disregarding what they don’t want to see. Our beliefs are a form of blindness.

    John Ioannidis, an epidemiologist at Stanford University, argues that such distortions are a serious issue in biomedical research. “These exaggerations are why the decline has become so common,” he says. “It’d be really great if the initial studies gave us an accurate summary of things. But they don’t. And so what happens is we waste a lot of money treating millions of patients and doing lots of follow-up studies on other themes based on results that are misleading.” In 2005, Ioannidis published an article in the Journal of the American Medical Association that looked at the forty-nine most cited clinical-research studies in three major medical journals. Forty-five of these studies reported positive results, suggesting that the intervention being tested was effective. Because most of these studies were randomized controlled trials—the “gold standard” of medical evidence—they tended to have a significant impact on clinical practice, and led to the spread of treatments such as hormone replacement therapy for menopausal women and daily low-dose aspirin to prevent heart attacks and strokes. Nevertheless, the data Ioannidis found were disturbing: of the thirty-four claims that had been subject to replication, forty-one per cent had either been directly contradicted or had their effect sizes significantly downgraded.

    The situation is even worse when a subject is fashionable. In recent years, for instance, there have been hundreds of studies on the various genes that control the differences in disease risk between men and women. These findings have included everything from the mutations responsible for the increased risk of schizophrenia to the genes underlying hypertension. Ioannidis and his colleagues looked at four hundred and thirty-two of these claims. They quickly discovered that the vast majority had serious flaws. But the most troubling fact emerged when he looked at the test of replication: out of four hundred and thirty-two claims, only a single one was consistently replicable. “This doesn’t mean that none of these claims will turn out to be true,” he says. “But, given that most of them were done badly, I wouldn’t hold my breath.”

    According to Ioannidis, the main problem is that too many researchers engage in what he calls “significance chasing,” or finding ways to interpret the data so that it passes the statistical test of significance—the ninety-five-per-cent boundary invented by Ronald Fisher. “The scientists are so eager to pass this magical test that they start playing around with the numbers, trying to find anything that seems worthy,” Ioannidis says. In recent years, Ioannidis has become increasingly blunt about the pervasiveness of the problem. One of his most cited papers has a deliberately provocative title: “Why Most Published Research Findings Are False.”

    The problem of selective reporting is rooted in a fundamental cognitive flaw, which is that we like proving ourselves right and hate being wrong. “It feels good to validate a hypothesis,” Ioannidis said. “It feels even better when you’ve got a financial interest in the idea or your career depends upon it. And that’s why, even after a claim has been systematically disproven”—he cites, for instance, the early work on hormone replacement therapy, or claims involving various vitamins—“you still see some stubborn researchers citing the first few studies that show a strong effect. They really want to believe that it’s true.”

    That’s why Schooler argues that scientists need to become more rigorous about data collection before they publish. “We’re wasting too much time chasing after bad studies and underpowered experiments,” he says. The current “obsession” with replicability distracts from the real problem, which is faulty design. He notes that nobody even tries to replicate most science papers—there are simply too many. (According to Nature, a third of all studies never even get cited, let alone repeated.) “I’ve learned the hard way to be exceedingly careful,” Schooler says. “Every researcher should have to spell out, in advance, how many subjects they’re going to use, and what exactly they’re testing, and what constitutes a sufficient level of proof. We have the tools to be much more transparent about our experiments.”

    In a forthcoming paper, Schooler recommends the establishment of an open-source database, in which researchers are required to outline their planned investigations and document all their results. “I think this would provide a huge increase in access to scientific work and give us a much better way to judge the quality of an experiment,” Schooler says. “It would help us finally deal with all these issues that the decline effect is exposing.”

    Although such reforms would mitigate the dangers of publication bias and selective reporting, they still wouldn’t erase the decline effect. This is largely because scientific research will always be shadowed by a force that can’t be curbed, only contained: sheer randomness. Although little research has been done on the experimental dangers of chance and happenstance, the research that exists isn’t encouraging.

    In the late nineteen-nineties, John Crabbe, a neuroscientist at the Oregon Health and Science University, conducted an experiment that showed how unknowable chance events can skew tests of replicability. He performed a series of experiments on mouse behavior in three different science labs: in Albany, New York; Edmonton, Alberta; and Portland, Oregon. Before he conducted the experiments, he tried to standardize every variable he could think of. The same strains of mice were used in each lab, shipped on the same day from the same supplier. The animals were raised in the same kind of enclosure, with the same brand of sawdust bedding. They had been exposed to the same amount of incandescent light, were living with the same number of littermates, and were fed the exact same type of chow pellets. When the mice were handled, it was with the same kind of surgical glove, and when they were tested it was on the same equipment, at the same time in the morning.

    The premise of this test of replicability, of course, is that each of the labs should have generated the same pattern of results. “If any set of experiments should have passed the test, it should have been ours,” Crabbe says. “But that’s not the way it turned out.” In one experiment, Crabbe injected a particular strain of mouse with cocaine. In Portland the mice given the drug moved, on average, six hundred centimetres more than they normally did; in Albany they moved seven hundred and one additional centimetres. But in the Edmonton lab they moved more than five thousand additional centimetres. Similar deviations were observed in a test of anxiety. Furthermore, these inconsistencies didn’t follow any detectable pattern. In Portland one strain of mouse proved most anxious, while in Albany another strain won that distinction.

    The disturbing implication of the Crabbe study is that a lot of extraordinary scientific data are nothing but noise. The hyperactivity of those coked-up Edmonton mice wasn’t an interesting new fact—it was a meaningless outlier, a by-product of invisible variables we don’t understand. The problem, of course, is that such dramatic findings are also the most likely to get published in prestigious journals, since the data are both statistically significant and entirely unexpected. Grants get written, follow-up studies are conducted. The end result is a scientific accident that can take years to unravel.

    This suggests that the decline effect is actually a decline of illusion. While Karl Popper imagined falsification occurring with a single, definitive experiment—Galileo refuted Aristotelian mechanics in an afternoon—the process turns out to be much messier than that. Many scientific theories continue to be considered true even after failing numerous experimental tests. Verbal overshadowing might exhibit the decline effect, but it remains extensively relied upon within the field. The same holds for any number of phenomena, from the disappearing benefits of second-generation antipsychotics to the weak coupling ratio exhibited by decaying neutrons, which appears to have fallen by more than ten standard deviations between 1969 and 2001. Even the law of gravity hasn’t always been perfect at predicting real-world phenomena. (In one test, physicists measuring gravity by means of deep boreholes in the Nevada desert found a two-and-a-half-per-cent discrepancy between the theoretical predictions and the actual data.) Despite these findings, second-generation antipsychotics are still widely prescribed, and our model of the neutron hasn’t changed. The law of gravity remains the same.

    Such anomalies demonstrate the slipperiness of empiricism. Although many scientific ideas generate conflicting results and suffer from falling effect sizes, they continue to get cited in the textbooks and drive standard medical practice. Why? Because these ideas seem true. Because they make sense. Because we can’t bear to let them go. And this is why the decline effect is so troubling. Not because it reveals the human fallibility of science, in which data are tweaked and beliefs shape perceptions. (Such shortcomings aren’t surprising, at least for scientists.) And not because it reveals that many of our most exciting theories are fleeting fads and will soon be rejected. (That idea has been around since Thomas Kuhn.) The decline effect is troubling because it reminds us how difficult it is to prove anything. We like to pretend that our experiments define the truth for us. But that’s often not the case. Just because an idea is true doesn’t mean it can be proved. And just because an idea can be proved doesn’t mean it’s true. When the experiments are done, we still have to choose what to believe. ?

    Read more http://www.newyorker.com/reporting/2010/12/13/101213fa_fact_lehrer#ixzz1As88Mr13

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