We need something akin to the international geophysical year, but for data integrity. Make it an interdisciplinary priority to clean house and root out papers that are hanging by a thread of included / excluded outliers, biased samples, and outright fraud. It would be humbling, but we'd be in much better shape afterwards.
The article suggests it's unreasonable numbers in the original Uzbekistan data source and that other datapoints may have been worse, the authors just didn't correctly execute their basic checks.
"It turns out that Uzbekistan wasn’t even the biggest outlier, but that the version they had published had the axes cropped so you couldn’t see the outliers..."
We need something akin to the international geophysical year, but for data integrity. Make it an interdisciplinary priority to clean house and root out papers that are hanging by a thread of included / excluded outliers, biased samples, and outright fraud. It would be humbling, but we'd be in much better shape afterwards.
I'm confused as to what the actual issue was. What was the data for which Uzbekistan was the outlier and why?
The article suggests it's unreasonable numbers in the original Uzbekistan data source and that other datapoints may have been worse, the authors just didn't correctly execute their basic checks.
"It turns out that Uzbekistan wasn’t even the biggest outlier, but that the version they had published had the axes cropped so you couldn’t see the outliers..."
My read is that the model had too much variance; more regularization was needed.