Smell you later

A few weeks ago, I started a new job as a data scientist at the logistics division of a pharmaceutical company. This was primarily motivated by family reasons. My wife works at an art museum in Chicago and could not find satisfactory work in my small college town. We'd been living apart while I struggled to find a new professorship that might put us in a city she'd like. The academic job market is lousy enough that I was lucky to have a job anywhere -- I was not the kind of research superstar and granting rainmaker who could work wherever I pleased.

The school going online in 2020 made it possible for me to spend a fourth year in academia, but with a big naughty dog and our first child on the way, there was no way I could return to in-person classes in the fall. I flailed around on LinkedIn looking for a chance to get into data science, but later lucked out when a headhunter found my resume on Dice and scooped me up.

So now I'm working in industry. I am happy with this development: the pay is good, I don't have to drive two and a half hours to see my wife, and I get to turn my brain off at 5pm. 

My impact on the field

I'd like to think that I've managed to do a few useful things in my time in social psychology.

• I've tried to point out that, contrary to some previous claims, there is reason to doubt that 15 minutes of violent video games causes a detectable increase in aggressive behavior. I've identified publication bias, and I've published null results.
• I've tried to bring greater awareness to publication bias and meta-analytic ways to adjust for it. The paper I wrote with Evan Carter, Felix Schonbrodt, and Will Gervais has been pretty successful in this light and quickly became my most cited paper.
• I've managed to get some really suspicious papers retracted. I'd like to think that this has helped shift the norms in post-publication review and retraction, if only slightly. I'm happy I could contribute a few more data points to the Retraction Watch database.
• Paul Bloom once saw me order a Sazerac at SPSP, asked me what it was, then ordered one himself. So you might say I've influenced some of the big names.

The stuff we do doesn't matter

The thing that I'll probably most miss about academia is getting to research whatever I'm curious about. I'm not missing it just yet, however. While on the tenure track, I didn't think I was discovering hidden truths about the human mind by studying the American college undergraduate and Prolific.co participant. 

Honestly, I'd felt pretty discouraged about research for a while. The things we study tend to have small effects, and when we can't detect those small effects a second time, it can be hard to tell why. (Possible explanations include noise, publication bias, errors in methods, differences in populations due to culture or even the passage of time.) It's why we spend so much time arguing about the fidelity of replication methods and hidden moderators.

Because the things we study have small, purportedly delicate effects, it's rare that we expect to see them applied and working in the real world. It's unpleasant to say it, but I feel that a lot of the research that we do doesn't matter. It's because it doesn't matter that we were able to get all the way into the 2010s before having a replication crisis. If we had screwed up our basic science in physics or biology or chemistry, we would notice pretty quickly when the engineers told us their bridges were collapsing or the crops were dying or the soda pop was going flat. By comparison, very little in social psychology seems to be applied or expected to work in any routinely detectable way.

The lackadaisical response I've sometimes received when raising concerns about papers has further convinced me that most social psych research does not matter. When I email a journal to say "none of these statistics add up" or "these effect sizes are ridiculously big," I often get no reply. Compare this to the sort of all-hands-on-deck response we might get if we found poison in the dog food. It doesn't matter that the product is no good -- we produce it for the sake of producing it, quality irrelevant.

In comparison, the stuff I'm doing as a data scientist isn't glamorous, but it's useful. Some of our projects save the company millions of dollars a year in shipping costs. That's a lot of gasoline and traffic and cardboard and dry ice that we're able to save. Reducing the amount of oil and packaging that gets used up might be the most useful thing I've done in years.

I sometimes wonder if the future of social psychology isn't in industry. Academics are spending their little budgets on MTurk studies and undergraduate surveys while tech companies have terabytes of data on people's activities (real people! from the real world!) and can run A/B tests whenever they like. People sometimes complain that "the best minds of our generation are working to get people to click on ads," but it's also the case that the best datasets of our generation are also dedicated to the same cause.

I also wasn't confident that I was doing anything useful as a teacher. At some level it broke my heart to know that students were paying money to attend my classes and take my exams. I probably expected too much, but I felt like my classes somehow had to lift my undergrads into a job. I could see a computer science course doing that, but not social psychology. I often felt like a failure for not being able to fix what generations of poverty and decades of underresourced primary schools had done to my students. Yes, there were a few exceptional students that I was able to help get into a job or a graduate program, but I feel like if it weren't me, it would have been another professor.

The Thumos Treadmill

Liam Kofi Bright has a really interesting philosophy article out on why academics are motivated to do fraud. In it, he examines acadmics through Plato's tripartite model of the soul: epithumia, the materialistic element, associated with the masses; thumos, the honor and esteem element, associated with the armed forces; and nous, the reason and wisdom element, associated with philosophers and scientists. Plato assumes that scientists are motivated by nous; Bright argues that they are also motivated by thumos, which has both good and bad consequences.

The neverending quest for thumos kind of drove me nuts. I didn't have a strategy for finding a new job that didn't revolve around just publishing as much as I could and hoping it would be enough. (I probably should have written a grant at some point, but I was too busy writing papers and too discouraged by terribly low funding rates.) It felt awful to stretch myself trying to build a better CV, knowing all the time that no matter how good my CV was, I still might not get what I wanted.

I liked writing papers, but they always felt like ten times more work than they should have been. And once finished, the interesting ones made for arguments and headaches and the quiet ones sank into the literature without a sound. I will admit that sometimes arguments tempted with the promise of a possible comment or reply, yielding another line on the CV at lower cost than a three-study empirical paper. For all my high-mindedness, I was still subject to the same pressures as everybody else.

I'm done with thumos for now. I'm enjoying the comparatively relaxed pace of my new job. I no longer have to try to become a one-in-a-thousand genius so I can get hired at an urban university; I can just be a guy who does his job well enough to not get fired. Maybe in a few years I'll get hungry for promotion to senior data scientist or something like that, but it'll be a while, if it ever happens. I've always been willing to earn a little less money in exchange for working a little less.

Normalize leaving academia

Academia is nice if it works out and you like the work and flexibility enough to take the pay dip. However, academia isn't going to go out of its way to take care of you. It can barely take care of itself.

If you're good at coding and data analysis, you can probably increase your salary by 50-100% by going to industry. Money isn't everything, but it's not nothing, either. I hate to be so capitalistic, but money feels a little bit like respect. It puts a clear and concrete value on your skills in a way that the occasional citation does not. After so much time begging for a tenure track position, getting a single offer by incredible good fortune, and going through it all over again trying to move to the city, it's nice to feel wanted again.

Being prepared to leave academia has benefits beyond the materialistic. You might recognize some of my more pugilistic works pointing out effects that are artifacts of selective removal of outliers, or effects that are too consistent or too big to be true, or the last two years of the Zhang affair. It can hurt your academic career to make enemies or to be known as a trouble-maker. Even outside of that, these projects had an opportunity cost; while I was writing criticism, I was not doing my own primary research and making discoveries with my name on them. Being ready to leave gave me the freedom and power to criticize what I felt needed to be criticized.

Spending any amount of time on the Zhang affair would have been a career mistake, of course, had I planned to stay. People are grateful to you for cleaning up the mess, but getting some papers retracted isn't going to get an entire department to want to hire you and work with you for the rest of your lives. Error detection isn't yet sustainable as a primary research interest; if it was, Elisabeth Bik would be an endowed chair.

If the NSF ever wants to assemble people for a real data police with a real budget, let me know. Until then, I'm going to be over here, writing code, cashing checks, and raising my family.

Thanks

I'd like to thank:

• Bruce Bartholow, my PhD advisor, for trusting me enough to do my thing as a graduate student, even when it involved making trouble.
• Laura King, who ran some good classes and an excellent journal club, and who was one of my letter-writers when I was looking for a job.
• Jeff Rouder, who was crucial to my development as a scientist and Bayesian, and who introduced me to R, which is now how I support my family.
• Daniel Lakens, whose early blog posts provided code for meta-analysis and PET-PEESE meta-regression, showing me that meta-analysis was just a single line of code, and not an arcane ritual requiring several degrees in rocket science. This opened a whole primary research interest to me.
• Illinois State University, for running a good psychology department where people generally get along. I'd like to particularly thank my department chair, Scott Jordan, for running a department where expectations are both reasonable and clear and professors aren't encouraged to eat each other alive.
• The Society for the Improvement of Psychological Science, particularly its early founders, for putting open science and post-publication peer review on the agenda. It's the only reason I was able to spend four years on the tenure track, and the only reason the science was worth doing.

In the roguelike community, people would post their "morguefiles" at the end of their game to show the thrilling way their character won or the tragicomic way they died. Here's mine:

        Joe_Hilgard the Assistant Professor (level 22)

             Began as a Social Psychologist

             Was a friend of SIPS.

             Escaped with a job

             ... and 31 publications on May 10, 2021!

             

             The professorship lasted 4 years.

Joe_Hilgard the Social Psychologist (HuSP)         

+3,+1 RStudio IDE {Int+4, Enhance}

+4 robe of Git {Dex+4 Int+2, Version Control}

+2 mug {Coffee}

+2 visored helmet "FunnelPlot" {Dam+3}

+2 gauntlets of emailing

+2 boots of BayesFactor

+1 ring of PubPeer

+2 ring of Monte Carlo

@: tired, grumpy, collecting data on college undergrads and MTurk workers, resistant to enchantments

}: 5 runes: meta-analytic, experimental, silver, iron, abyssal

You escaped.

You attended 4 universities and accumulated 22 years of education.

You visited the Abyss 2 times.

You visited 1 Labyrinth.

Your h-index was 18.

You got other authors to retract 5 articles.

You had 261 unread emails.

You owed your collaborators 5 overdue action items.

You signed your peer reviews.

        #.#######.#     #.#######

        #.........#     #........

        #......##############.###

        #......#.8#.##.#### #.#

        #......#<.........# #.#

        ########...8..8..8###.#

               #<.........'...#

               #...8..8..8#####

               #@.........#

               #.8#.##.##.#

               ##########.#

                        #.#

                        #.#######

                        #......Wp

                        #^#######

                    #####.#     #

#####################.....#     #

Message History

You preprocess your data.

Ping! New email.

You hold Zoom meetings with students.

Ping! New email.

The fraudster publishes!

The fraudster publishes!

The fraudster publishes!

Publish which blog post? (* to show all)

People are talking about the blog post.

Ping! New email.

A fraudulent paper is retracted!

You hold Zoom meetings with students.

You hold Zoom meetings with students.

There is a job offer at a company here.

Are you sure you want to win?

You have escaped!

I tried to report scientific misconduct. How did it go?

This is the story of how I found what I believe to be scientific misconduct and what happened when I reported it.

Science is supposed to be self-correcting. To test whether science is indeed self-correcting, I tried reporting this misconduct via several mechanisms of scientific self-correction. The results have shown me that psychological science is largely defenseless against unreliable data.

I want to share this story with you so that you understand a few things. You should understand that there are probably a few people in your field producing work that is either fraudulent or so erroneous it may as well be fraudulent. You should understand that their work is cited in policy statements and included in meta-analyses. You should understand that, if you want to see the data or to report concerns, those things happen according to the inclinations of the editor-in-chief at the journal. You should understand that if the editor-in-chief is not inclined to help you, they generally not accountable to anyone and they can always ignore you until the statute of limitations runs out.

Basically, it is very easy to generate unreliable data, and it is very difficult to get it retracted.

Qian Zhang

Two years ago, I read a journal article that appeared to have gibberish for all its statistics (Zhang, Espelage, & Zhang, 2018). None of the numbers in the tables added up: the p values didn't match the F values, the F values didn't match the means and SDs, and the degrees of freedom didn't match the sample size. This was distressing because the sample size was a formidable 3,000 participants. If these numbers were wrong, they were going to receive a lot of weight in future meta-analyses. I sent the editor a note saying "Hey, none of these numbers make sense." The editor said they'd ask the authors to correct, and I moved on with my life.

 

Figure 1. Table from Zhang, Espelage, & Zhang, (2018). The means and SDs don’t make sense, and the significance asterisks are incorrect given the F values.

Then I read the rest of Dr. Zhang's first-authored articles and realized there was a broader, more serious problem – one that I am still spending time and energy trying to clean up, two years later.

 

Problems in Qian Zhang’s articles

Zhang’s papers would often report impossible statistics. Many papers had subgroup means that could not be combined to yield the grand mean. For example, one paper reported mean task scores of 8.98ms and 6.01ms for males and females, respectively, but a grand mean task score of 23ms.

Other papers had means and SDs that were impossible given the range. For example, one study reported a sample of 3,000 children with ages ranging from 10 to 20 years (M = 15.76, SD = 1.18), of which 1,506 were between ages 10 and 14 and 1,494 were between ages 15 and 20. If you put those numbers into SPRITE, you will find that, to meet the reported mean and SD of age, all the participants must be between the ages of 14 and 19, and only about 500 participants could be age 14.

More seriously still, tables of statistical output seemed to be recycled from paper to paper. Two different articles describing two different experiments on two different populations would come up with very similar cell means and F values. Even if one runs exactly the same experiment twice, sampling error means that the odds of getting all six cells of a 2 × 3 design to come up again within a few decimal points are quite low. The odds of getting them on an entirely different experiment years later in a different population would be smaller still.

As an example, consider this table, published in Zhang, Espelage, and Rost (2018), Youth and Society (Panel A), in which 2,000 children (4th-6th grade) perform a two-color emotion Stroop task. The means and F values closely match the same values as a sample of 74 high schoolers (Zhang, Xiong, & Tian, 2013, Scientific Research: Health, Panel B) and a sample of 190 high schoolers (Zhang, Zhang, & Wang, 2013, Scientific Research: Psychology, Panel C).

Figure 2. Three highly similar tables from three different experiments by Zhang and colleagues. The degree of similarity for all nine values of the table is suspiciously high.

Dr. Zhang publishes some corrigenda 

After my first quick note to Youth and Society that Zhang’s p values didn't match the F values, Dr. Zhang started submitting corrections to journals. What was remarkable about these corrections is that they would simply add an integer to the F values so that they would be statistically significant.

Consider, for example, this correction at Personality and Individual Differences (Zhang, Tian, Cao, Zhang, & Rodkin, 2016):

Figure 3. An uninterpretable ANOVA table is corrected by the addition or subtraction of an integer value from its F statistics.

The correction just adds 2 or 3 onto the nonsignificant F values to make them match their asterisks, and it subtracts 5 from the significant F value to make it match its lack of asterisks.

Or this correction to Zhang, Espelage, and Zhang (2018), Youth and Society, now retracted:

 

Figure 4. Nonsignificant F values become statistically significant through the addition of a tens digit. Note that these should now have three asterisks rather than one and two, respectively.

Importantly, none of the other summary or inferential statistics had to be changed in these corrigenda, as one might expect if there was an error in analysis. Instead, it was a simple matter of clobbering the F values so that they’d match the significance asterisks.

Asking for raw data

While I was investigating Zhang’s work from 2018 and earlier, he published another massive 3,000-participant experiment in Aggressive Behavior (Zhang et al., 2019). Given the general sketchiness of the reports, I was getting anxious about the incredible volume of data Zhang was publishing. 

I asked Dr. Zhang if I could see the data from these studies to try to understand what had happened. He refused, saying only the study team could see the data. 

So, I decided I’d ask the study team. I asked Zhang’s American co-author if they had seen the data. They said they hadn't. I suggested they ask for the data. They said Zhang refused. I asked them if they thought that was odd. They said, no, "It's a China thing."

 

Reporting Misconduct to the Institution

Given the recycling of tables across studies, the impossible statistics, the massive sample sizes, the secrecy around the data, and the corrigenda which had simply bumped the F values into significance, I suspected I had found research misconduct.  In May 2019, I wrote up a report and sent it to the Chairman of the Academic Committee at his institution, Southwest University Chongqing. You can read that report here.

A month later, I was surprised to get an email from Dr. Zhang. It was the raw data from the Youth & Society article I had previously asked for and been refused.

Looking at the raw data revealed a host of suspicious issues. For starters, participants were supposed to be randomly assigned to movie, but girls and students with high trait aggression were dramatically more likely to be assigned to the nonviolent movie. 

There was something else about the reaction time data that is a little more technical but very serious. Basically, reaction time data on a task like the Stroop should show within-subject effects (some conditions have faster RTs than others) and between-subject effects (some people are faster than others). Consequently, even an incongruent trial from Quick Draw McGraw could be faster than a congruent trial from Slowpoke Steven.

Because of these between-subject effects, there should be a correlation between a subject’s reaction times in one condition and their reaction times in the other. If you look at color-Stroop data I grabbed from a reliable source on the OSF, you can see that correlation is very strong. 

Figure 5. The correlation between subjects' mean congruent-word RT and mean incongruent-word RT in a color-word Stroop task. Data from Lin, Inzlicht, Saunders, & Friese (2019).

If you look at Zhang’s data, you see the correlation is completely absent. You might also notice that the distribution of subjects’ means is weirdly boxy, unlike the normal or log-normal distribution you might expect.

Figure 6. The correlation between subjects' mean aggressive-word RT and nonaggressive-word RT in an aggressive-emotion Stroop task. Data from Zhang, Espelage, and Rost (2018). The distribution of averages is odd, and the correlation unusually weak.

There was no way the study was randomized, and there was no way that the study data was reliable Stroop data. I wrote an additional letter to the institution detailing these oddities. You can read that additional letter here.

A month after that, Southwest University cleared Dr. Zhang of all charges.

The letter I received declared: "Dr. Zhang Qian was deficient in statistical knowledge and research methods, yet there is insufficient evidence to prove that data fraud [sic]." It explained that Dr. Zhang was just very, very bad at statistics and would be receiving remedial training and writing some corrigenda. The letter noted that, as I had pointed out, the ANOVA tables were gibberish and the degrees of freedom did not match the reported sample sizes. It also noted that the "description of the procedure and the object of study lacks logicality, and there is a suspicion of contradiction in the procedure and inconsistency in the sample," whatever that means.

However, the letter did not comment on the strongest pieces of evidence for misconduct: the recycled tables, the impossible statistics, and the unrealistic properties of the raw data. I pressed the Chairman for comment on these issues. 

After four months, the Chairman replied that the two experts they consulted determined that "these discussions belong to academic disputes." I asked to see the report from the experts. I did not receive a reply.

 

Reporting Misconduct to the Journals

The institution being unwilling to fix anything, I decided to approach the journals. In September and October 2019, I sent each journal a description of the problems in the specific article each had published, as well as a description of the broader evidence for misconduct across articles. 

I hoped that these letters would inspire some swift retractions, or at least, expressions of concern. I would be disappointed.

Some journals appeared to make good-faith attempts to investigate and retract. Other journals have been less helpful.

The Good Journals

Youth and Society reacted the most swiftly, retracting both articles two months later. 

Personality and Individual Differences took 10 months to decide to retract. In July 2020, the editor showed me a retraction notice for the article. I am still waiting for the retraction notice to be published. It was apparently lost when changing journal managers; once recovered, it then had to be sent to the authors and publisher for another round of edits and approvals.

Computers in Human Behavior is still investigating. The editor received my concerns with an appropriate degree of attention, but it seems there was some confusion about whether the editor or the publisher is supposed to investigate that has slowed down the process.

I felt these journals generally did their best, and the slowness of the process likely comes from the bureaucracy of the process and the inexperience editors have with that process. Other journals, I felt, did not make such an attempt.

Aggressive Behavior

In October 2019, Zhang sent me the data from his Aggressive Behavior article. I found the data had the same bizarre features that I had found when I received the raw data from Zhang's now-retracted Youth and Society article. I wrote a letter detailing my concerns and sent it to Aggressive Behavior's editor in chief, Craig Anderson. 

The letter, which you can read here, detailed four concerns. One was about the plausibility of the average Stroop effect reported, which was very large. Another was about failures of random assignment: chi-squared tests found the randomly-assigned conditions differed in sex and trait aggression, with p values of less than one in a trillion. The other two concerns regarded the properties of the raw data.

It took three months and two emails to the full editorial board to receive acknowledgement of my letter. Another four months after that, the journal notified me that it would investigate. 

Now, fifteen months after the submission of my complaint, the journal has made the disappointing decision to correct the article. The correction explains away the failures of randomization as an error in translation; the authors now claim that they let participants self-select their condition. This is difficult for me to believe. The original article’s stressed multiple times its use of random assignment and described the design as a "true experiment.” They also had perfectly equal samples per condition ("n = 1,524 students watched a 'violent' cartoon and n = 1,524 students watched a 'nonviolent' cartoon.") which is exceedingly unlikely to happen without random assignment. 

The correction does not mention the multiple suspicious features of the raw data. 

This correction has done little to assuage my concerns. I feel it is closer to a cover-up. I will express my displeasure with the process at Aggressive Behavior in greater detail in a future post.

 

Zhang’s newest papers

Since I started contacting journals, Zhang has published four new journal articles and one ResearchSquare preprint. I also served as a peer reviewer on two of his other submissions: One was rejected, and the other Zhang withdrew when I repeatedly requested raw data and materials.

These newest papers all carefully avoid the causes of my previous complaints. I had complained it was unlikely that Zhang should collect 3,000 subjects every experiment; the sample sizes in the new studies range from 174 to 480. I had complained that the distribution of aggressive-trial and nonaggressive-trial RTs within a subject didn’t make sense; the new studies analyze and present only the aggressive-trial RTs, or they report a measure that does not require RTs.

Two papers include a public dataset as part of the online supplement, but the datasets contain only the aggressive-trial RTs. When I contacted Zhang, he refused to share the nonaggressive-trial RTs. He has also refused to share the accuracy data for any trials. This might be a strategy to avoid tough questions about the kind of issues I found in his Youth & Society and Aggressive Behavior articles. 

Because Zhang refused me access to the data, I had to try asking the editors at those journals to enforce the APA Code of Ethics section 8.14 which requires sharing of data for the purpose of verifying results.

At Journal of Experimental Child Psychology, I asked editor-in-chief David Bjorklund to intervene. Dr. Bjorklund has asked Dr. Zhang to provide the requested data. I thank him for upholding the Code of Ethics. A month and half have passed since Dr. Bjorklund's intervention, and I yet to receive the requested data and materials from Dr. Zhang.

At Children and Youth Services Review, I asked editor-in-chief Duncan Lindsey to intervene. Zhang claimed that the data consisted only of aggressive-trial RTs, and that he could not share the program because it “contained many private information of children and had copyrights.”

I explained my case to Lindsey. Lindsey sent me nine words — "You will need to solve this with the authors." — and never replied again.

Dr. Lindsey's failure to uphold the Code of Ethics at his journal is shameful. Scholars should be aware that Children and Youth Services Review has chosen not to enforce data-sharing standards, and research published in Children and Youth Services Review cannot be verified through inspection of the raw data.

I have not yet asked for the data behind Zhang’s new articles in Cyberpsychology, Behavior, and Social Networking or Journal of Aggression, Maltreatment, & Trauma.

Summary

I was curious to see how the self-correcting mechanisms of science would respond to what seemed to me a rather obvious case of unreliable data and possible research misconduct. It turns out Brandolini’s Law still holds: “The amount of energy needed to refute bullshit is an order of magnitude larger than to produce it.” However, I was not prepared to be resisted and hindered by the self-correcting institutions of science itself.

I was disappointed by the response from Southwest University. Their verdict has protected Zhang and enabled him to continue publishing suspicious research at great pace. However, this result does not seem particularly surprising given universities' general unwillingness to investigate their own and China's general eagerness to clear researchers of fraud charges.

I have also generally been disappointed by the response from journals. It turns out that a swift two-month process like the one at Youth and Society is the exception, not the norm.

In the cases that an editor in chief has been willing to act, the process has been very slow, moving only in fits and starts. I have read before that editors and journals have very little time or resources to investigate even a single case of misconduct. It is clear to me that the publishing system is not ready to handle misconduct at scale.

In the cases that an editor in chief has been unwilling to act, there is little room for appeal. Editors can act busy and ignore a complainant, and they can get indignant if one tries to go around them to the rest of the editorial board. It is not clear who would hold the editors accountable, or how. I have little leverage over Craig Anderson or Duncan Lindsey besides my ability to bad-mouth them and their journals in this report. At best, they might retire in another year or two and I could have a fresh editor with whom to plead my case.

The clearest consequence of my actions has been that Zhang has gotten better at publishing. Every time I reported an irregularity with his data, his next article would not feature that irregularity. In essence, each technique for pointing out the implausibility of the data can be used only once, because an editor’s or university’s investigation consists of showing the authors all the irregularities and asking for benign explanations. This is a serious problem when even weak explanations like “I didn’t understand what randomized assignment means” or “I’m just very bad at statistics” are considered acceptable.

Zhang has reported experiments with sample sizes totaling to more than 11,000 participants (8,000 given the Aggressive Behavior correction). This is an amount of data that rivals entire meta-analyses and ManyLabs projects. If this data is flawed, it will have serious consequences for reviews and meta-analyses.

In total, trying to get these papers retracted has been much more difficult, and rather less rewarding, than I had expected. The experience has led me to despair for the quality and integrity of our science. If data this suspicious can’t get a swift retraction, it must be impossible to catch a fraud equipped with skills, funding, or social connections.