Errors in scientific research are all too common, and the problem has been getting worse. We’ve been led to believe that the methods of science are self-correcting, which is true, but only if they’re understood and followed, which is seldom the case. Ignorance of robust scientific methodology varies among disciplines, but it’s hard to imagine that any discipline can do worse than the errors that I’ve encountered in the field of information visualization.
An alarming article, “Trouble at the Lab,” in the October 19, 2013 edition of The Economist provides keen insight into the breadth, depth, and causes of this problem in academic research as a whole.
Academic scientists readily acknowledge that they often get things wrong. But they also hold fast to the idea that these errors get corrected over time as other scientists try to take the work further. Evidence that many more dodgy results are published than are subsequently corrected or withdrawn calls that much-vaunted capacity for self-correction into question. There are errors in a lot more of the scientific papers being published, written about and acted on than anyone would normally suppose, or like to think.
Various factors contribute to the problem. Statistical mistakes are widespread. The peer reviewers who evaluate papers before journals commit to publishing them are much worse at spotting mistakes than they or others appreciate. Professional pressure, competition and ambition push scientists to publish more quickly than would be wise. A career structure which lays great stress on publishing copious papers exacerbates all these problems. “There is no cost to getting things wrong,” says Brian Nosek, a psychologist at the University of Virginia who has taken an interest in his discipline’s persistent errors. “The cost is not getting them published.”
Graduate students are strongly encouraged by professors to get published, in part because the professor’s name will appear on the published study, even if they’ve contributed little, and professors don’t remain employed without long and continually growing lists of publications. In the field of information visualization, most of the students who do these studies have never been trained in research methodology, and it appears that most of their professors have skipped this training as well. It might surprise you to hear that most of these students and many of the professors also lack training in the fundamental principles and practices of information visualization, which leads to naïve mistakes. This is because most information visualization programs reside in computer science departments, and most of what’s done in computer science regarding information visualization, however useful, does not qualify as scientific research and does not involve scientific methods. There are exceptions, of course, but overall the current state of information visualization research is dismal.
The peer review system is not working. Most reviewers aren’t qualified to spot the flaws that typically plague information visualization research papers. Those who are qualified are often unwilling to expose errors because they want to be liked, and definitely don’t want to set themselves up as a target for a tit-for-tat response against their own work. On several occasions when I’ve written negative reviews of published papers, friends of mine in the academic community have written to thank me privately, but have never been willing to air their concerns publicly—not once. Without a culture of constructive critique, bad research will continue to dominate our field.
Papers with fundamental flaws often live on. Some may develop a bad reputation among those in the know, who will warn colleagues. But to outsiders they will appear part of the scientific canon.
Some of the worst information visualization papers published in the last few years have become some of the most cited. If you say something (or cite something) often enough, it becomes truth. We’ve all heard how people only use 10% of their brains. This is common knowledge, but it is pure drivel. Once the media latched onto this absurd notion, the voices of concerned neuroscientists couldn’t cut through the confusion.
How do we fix this? Here are a few suggestions:
- Researchers must be trained in scientific research methods. This goes for their professors as well. Central to scientific method is a diligent attempt to disprove one’s hypotheses. Skepticism of this type is rarely practiced in information visualization research.
- Researchers must be trained in statistics. Learning to get their software to spit out a p-value is not enough. Learning what a p-value means and when it should be used is more important than learning to produce one.
- Rigid standards must be established and enforced for publication. The respected scientific journal Nature has recently established an 18-point guideline for authors. Most of the guidelines that exist for information visualization papers are meager and in many cases counter-productive. For example, giving high scores for innovation encourages researchers to prioritize novelty over usefulness and effectiveness.
- Peer reviewers must be carefully vetted to confirm that they possess the required expertise.
- Rigid guidelines must be established for the peer review process.
- Peer review should not be done anonymously. I no longer review papers for most publications because they require reviewers to remain anonymous, which I refuse to do. No one whose judgment affects the work of others should be allowed to remain anonymous. Also, anyone who accepts poorly done research for publication should be held responsible for that flawed judgment.
- Researchers should be encouraged to publish their work even when it fails to establish what they expected. The only failure in research is research done poorly. Findings that conflict with expectations are still valuable findings. Even poorly done research is valuable if the authors admit their mistakes and learn from them.
- Researchers should be encouraged to replicate the studies of others. Even in the “hard sciences,” most published research cannot be successfully replicated. One of the primary self-correcting practices of science is replication. How many information visualization papers that attempt to replicate research done by others have you seen? I’ve seen none.
I’m sure that other suggestions belong on this list, but these of the ones that come to mind immediately. Many leaders in the information visualization community have for years discussed the question, “Is data visualization science?” My position is that it could be and it should be, but it won’t be until we begin to enforce scientific standards. It isn’t easy to whip a sloppy, fledgling discipline into shape and you won’t win a popularity contest by trying, but the potential of information visualization is too great to waste.