It’s about magic, or something very close to magic: statistical control. Neurocopiae talks covariates.
In psychology, there is no such thing as a perfect experiment. Often, it is clear from the get-go that there are certain problems (“confounds”) you will not be able to eliminate, no matter how sophisticated your design might be. The remedy is simple and straightforward: measure what you can measure and try to statistically control for these variables. From some enthusiastic applications, I find that the number of covariates appears to be proportional to the degree you want to show off to other researchers how deeply you care about your data. As a psychologist, I can say that we love and embrace covariates (and subtle, yet significant interaction terms, but this practice had some bad press lately). The belief in the statistical procedure of control via covariates thus seems to be deeply rooted in practice, a bit of everyday numerical magic. Continue reading “Covariate magic part 1: That has been accounted for by the covariate!”
There is a lot of buzz around brain stimulation, but new problems start to surface. Neurocopiae reviews news on bad practices and poor reliability.
It hasn’t been a very good week for proponents of the popular brain stimulation method called transcranial direct current stimulation (tDCS). tDCS is a non-invasive technique that uses electrodes to deliver weak current to a person’s forehead. Numerous papers have claimed that tDCS can enhance mood, alleviate pain, or improve cognitive function. Such reports have sparked interest in tDCS at a broader scale. When you enter tDCS in the youtube search, you will find DIY tutorials on how to assemble a device so that you can amp up your brain at home. Including enthusiastic reports of the resulting changes in brain function. To put it in Richard Dawkins’ words: Science? It works, bitches. In particular, it works when you know what the outcome should be. Continue reading “Amping up control? Bad research practices and poor reliability raise concerns about brain stimulation”
Neurocopiae takes a closer look at the carefully crafted pizza study survey by the Wansink lab.
UWhen it comes to reheating leftover pizza, opinions are typically divided. I like cold pizza better because when you reheat a slice of pizza, it gets soggy. This soggy slice of pizza is a fitting metaphor for the next chapter in the Wansink pizzagate saga. I was a bit reluctant to write another post on the sad downfall of ig-nobel laureate Brian Wansink, head of the Food & Brand lab at Cornell University [Mindless publishing garnished with social science apologies], but I had to take a look at the now infamous pizza buffet data myself. A couple of days ago, Wansink posted a statement re-emphasizing that “[he], of course, take[s] accuracy and replication of our research results very seriously.” More importantly, Wansink finally granted access to the data that the four papers, which came under fire months ago, were based on: “My team has also worked to make the full anonymized data and scripts for each study available for review.” This is awesome because everything is settled now, right? Move on, methodological terrorists, nothing to see here. Well, almost. Continue reading “When you handle trash, do you still have to handle it with statistical care?”
Life is hard, science is harder, social science is impossible? Neurocopiae has to digest a bottomless dump of “fun” results.
Last time, I wrote a post about how difficult it is to do good research on nutrition and health (Cereal killer: Is eating breakfast the new smoking?). A couple of weeks later, as the pizzagate unfolds, we painfully learn more about these intricacies slice by slice. At the center of attention is Brian Wansink, who “is Professor and Director of the famed Cornell University Food and Brand Lab, where he is a leading expert in changing eating behavior“. If you have missed the start of the controversy and feel like you need to catch up on the full narrative, I have linked a good summary by Andrew Gelman. Briefly, Wansink wrote a post on his blog. He provided the career advice to never say no to your supervisor’s proposals because this is how you will get tenure by publishing numerous papers. Even if you have a dataset at hand that does not yield the expected result, you can torture it for a while until it finally surrenders and provides one or more significant results. Now, all it takes is little more deep diving into the data and a little pinch of wild story-telling and there you go: you have successfully inflated your list of publications. Treated in this do-or-die way, every study turns into science equivalent of the bottomless soup bowl that Wansink became famous for. Continue reading “Mindless publishing garnished with social science apologies”
2016 draws to a close and New Year’s resolutions are just around the corner. Neurocopiae talks about intentions to lose weight and showcases google Trends. The trend is your friend.
As the year 2016 draws to a close, it is the perfect opportunity to look back at what we have accomplished and what lies ahead of us. New Year’s resolutions are a popular way to set ambitious goals. Yet, a new year does not magically bestow us with the willpower and persistence to succeed. This is why such resolutions have garnered a bad reputation in the press lately as if we were set to fail. However, the fate of our resolutions is barely more remarkable than the fate of the many good intentions that we fail to put into practice every day. Regardless of the season, intention is simply a bad predictor of long-term action. No need to heap blame on the New Year. Continue reading “‘Tis the season, but not for weight loss”
The world is not the same after Trump’s election and this blog is no different. Neurcopiae explores how we can learn from the failure of prediction models.
If casting predictions is your bread and butter, you know how hard it is to be spot on. Luckily, in most cases it does not matter when we happen to be a bit off target because the implications are modest at best. This is why every prediction comes with a margin of error or a confidence interval. Still, when Trump defied the odds of poll predictions on election night and edged out the victory, I felt deeply troubled. Stats let me down on this important occasion and it was tough to take. Continue reading “When the margin of error is decisive: Trump’s victory as a lesson for neuroscience, part 1”