Running scientific experiments is, frankly, a pain in the ass. Sure, it's incredibly satisfying when days or weeks of hard work produce a clean-looking result that's easy to interpret. But often as not, experiments simply fail for no obvious reason. Even when they work, the results often leave you scratching your head, wondering "what in the world is that supposed to tell me?"

The simplest solution to these problems is obvious: don't do experiments. (Also, don't go out into the field to collect data, which adds the hazards of injury, sunburn, and exotic disease to the mix.) Unfortunately, data has somehow managed to become the foundation of modern science—so you're going to need to get some from somewhere if you want a career. A few brave souls have figured out a way to liberate data from the tyranny of experimentation: they simply make it up.

Dr. Yoshitaka Fujii seems well on his way to becoming the patron saint of scientific fraudsters, setting a record for the most extensive output of fake data. As near as anyone can work out, Fujii started making up data with abandon some time in the 1990s. By 2000, his fellow researchers were already on to him, publishing a comment in which they noted, "We became skeptical when we realized that side effects were almost always identical in all groups."

But you can't let such skepticism from your peers slow you down—and Fujii certainly didn't. Even after the comment was published, two different medical schools hired him as a faculty member. He continued to publish, generally using faked data, racking up an eventual record of 200+ bogus papers.

Nobody took any responsibility for investigating the prospect of fraud, despite requests made by other researchers who suspected something was amiss. It took until 2011 for the editors of several journals that were victimized by Fujii to band together and hire an outside investigator, who found extensive evidence that the data reported by Fujii was unlikely to have resulted from actual experiments. That finally prompted Toho University, his current employer, to launch its own investigation (PDF). Conclusion: almost none of Fujii's publications were free of falsified data.

Decades of scientific fraud simply shouldn't be this easy. Yet Fujii, along with a few other serial fraudsters, have somehow managed it year after year. In tribute to his staggering success, Ars presents this handy guide on how to get away with faking your data, based on the most popular techniques used in the biggest cases of scientific fraud (so far). Hopefully, it will help answer one of the key questions looming over the Fujii story: In a world of hard data and peer review, just how was such a colossal fraud even possible?

The tao of the fraudster

Fake data nobody ever expects to see. If you're going to make things up, you won't have any original data to produce when someone asks to see it. The simplest way to avoid this awkward situation is to make sure that nobody ever asks. You can do this in several ways, but the easiest is to work only with humans. Most institutions require a long and painful approval process before anyone gets to work directly with human subjects. To protect patient privacy, any records are usually completely anonymized, so no one can ever trace them back to individual patients. Adding to the potential for confusion, many medical studies are done double-blind and use patient populations spread across multiple research centers.

All of these factors make it quite difficult for anyone to keep track of the original data, and they mean that most people will be satisfied with using a heavily processed version of your results. That makes faking the data much easier. The classic example here is Jon Sudbø, who apparently made up even the patients used in a number of studies. He was only caught because someone working for the Norwegian public health institute couldn't figure out where he had possibly obtained some of his results; the fake data itself never aroused suspicions prior to that point.

If you still want to provide real—well, "real"—underlying data, one option is to use one-of-a-kind equipment, since nobody else will be likely to make sense out of the raw data, anyway. But really, the hassle and expected anonymity involved in working with human subjects is the most convenient data screen of all.

Work with many collaborators. This has several advantages. For one, it helps ensure that your fake data is unlikely to be the centerpiece of a given report, and thus less likely to attract scrutiny. Second, your fraud will also bask in the borrowed credibility of all the solid research that surrounds it. Finally, it keeps other scientists in your field guessing. If you handle things well, even your own collaborators may not know who supplied which data from what experiments, making the whole deception more difficult to untangle.

This technique worked well for Fujii, who found it so helpful that he started adding collaborators to his papers without bothering to tell them that he had done so. In the rare cases where journals actually required some form of acknowledgement from the collaborators, Fujii just... forged their signatures. Simple and elegant.

Tell people what they already know. Isaac Asimov is credited with saying, "The most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka' but 'That’s funny...'" Since you don't want anyone excited about your work, due to the likelihood they will ask annoying questions, you need to avoid this reaction at all costs. Under no circumstances should your work cause anyone to raise an intrigued eyebrow.

The easiest way to do this is to play to people's expectations, feeding them data that they respond to with phrases like "That's about what I was expecting." Take an uncontroversial model and support it. Find data that's consistent with what we knew decades ago. Whatever you do, don't rock the boat.

Don't do research anyone cares about. This is a close relative of point three, but with a subtle distinction—people may care about your work even if they don't find it strange. They may even want to repeat it with a larger population. They may want to try one of your techniques in a different context. They may think up novel ways to extend it to other areas.

Although this might be exactly the sort of thing you want to see happen if you were performing actual research, it's a potential disaster for the aspiring fraudster. The last thing you want is for anyone to look over your work in enough detail to repeat some aspect of it. The trick is to keep producing papers that are just good enough to be published—but not good enough that anyone else will want to follow up on them.

Fujii had this down (if you'll pardon the pun) to a science. With a few exceptions, the papers he wrote were cited less than a handful of times by other scientists. Despite an impressive research output and a fat C.V., most of his work made no impression on his fellow scientists. Which, for him, was a good thing.