You and I know that there’s the equivalent of a Berlin wall between the self-styled hard sciences and the so-called soft [read “flaccid”] sciences, to which group most of the readers and I belong. Well, it appears as if that demarcation line has gone from solid to dashed—from im- to permeable. And the news is percolating into the mainstream.
On September 10th, 2013, the good citizens over at Phys.org—Fiona Fidler and Ascelin Gordon—asked the nearly existential question
“Science is in a reproducibility crisis: How do we resolve it?”
“What?” you say You’re “Shocked!” Shocked to find that the reproducible experiment isn’t a ticket to objective truth? Shocked to find charlatans, fakirs, and stupids amongst all those bright, shiny Nobel Prize winners?
Well. I’m not. But this isn’t about me.
Let’s see what Fidler and Gordon have to say.
They start with a couple of examples that have stirred up the fear in the experimental sciences. Some psychology journal published a finding of extrasensory perception. A number of cancer studies have not been replicated. The psychology story is a real hoot. Who really cares. But, cancer studies? No wonder I’m eating butter again after eating no-trans-fat margarine for a long time after being told that any margarine was better for me than real butter!
The authors point to the following as possible culprits in this breakdown of credibility [or is it an up-tick in credulity?]
mechanised reporting of statistical results and publication bias towards “statistically significant” results.
softer fraud—or “undisclosed flexibility” in data collection—is well documented and appears to be very widespread.
Data sharing . . . can be time-consuming, and currently provide little academic reward.
Funding bodies and academic journals that value “novelty” over replication deserve blame too.
|J. R. R. Tolkein’s portrait of Smaug
protecting the dwarf treasure.
I presume they mean that ‘hard’ science suffers from the GIGO syndrome—Garbage In; Garbage Out—when research depends on highly complex statistical analyses that originate from algorithms that no one but the code writer knows anything about! Likewise, there are many scientists playing Smaug the Dragon, and planting themselves threateningly on any pile [‘scuse the expression] of data they generated.
But, help is on the way through ‘initiatives’ aimed at stemming the flow of bad research. One found me laughing out loud.
there’s the Reproducibility Initiative. . . . backed by [among others] . . . , the journal PLOS ONE . . . to be published in PLOS ONE.
a “reproducibility index” for journals, similar to an impact factor . .
There’s a non-starter for you. The authors then relate another pie-in-the-sky notion, needed changes
[to the way scientists] prepare, submit and peer review journal articles, as well as changes in how science is funded.
open peer-review, and open notebook science
‘Nother two non-starters, there. Now, here’s a good one, which I’ve said many times would have helped me in being authoritatively critical of archaeological quantifications.
Publishing computer source code and supporting data sets . . . a greater incentive to publish their data by making scientific datasets citable contributions . . .
But, joy turns to disappointment. Alas,
In many areas of science, researchers are not trained in data curation, version control of source code or other methodologies required for research to be replicable.
I’m slack jawed at this moment. Not trained? How is that even thinkable?
Today’s take-home message. Avoid acting smug around ‘hard’ scientists.
Any suggestion that theirs might not be as hard as they’ve been maintaining all these years is likely to provoke violence.
I hope I’m leaving you with a smile. I’m reminded of a satirical gender joke from the 70s .
Question: “Why is it that women don’t make good carpenters?
Answer: “Because for years men have been telling them, as they hold their grimy index fingers out in front of them—about 6 inches apart—that the distance thus indicated was a foot.”
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