Megan McArdle points to this story about trying to create infant mortality data out of thin air:
Of the 193 countries covered in the study, the researchers were able to use actual, reported data for only 33. To produce the estimates for the other 160 countries, and to project the figures backwards to 1995, the researchers created a sophisticated statistical model. What’s wrong with a model? Well, 1) the credibility of the numbers that emerge from these models must depend on the quality of “real” (that is, actual measured or reported) data, as well as how well these data can be extrapolated to the “modeled” setting ( e.g. it would be bad if the real data is primarily from rich countries, and it is “modeled” for the vastly different poor countries – oops, wait, that’s exactly the situation in this and most other “modeling” exercises) and 2) the number of people who actually understand these statistical techniques well enough to judge whether a certain model has produced a good estimate or a bunch of garbage is very, very small.
Without enough usable data on stillbirths, the researchers look for indicators with a close logical and causal relationship with stillbirths. In this case they chose neonatal mortality as the main predictive indicator. Uh oh. The numbers for neonatal mortality are also based on a model (where the main predictor is mortality of children under the age of 5) rather than actual data.
So that makes the stillbirth estimates numbers based on a model…which is in turn…based on a model.
This sound familiar to anyone? The only reason it is not a good analog to climate is that the article did not say that they used mortality data from 1200 kilometers away to estimate a country’s historic numbers.
Smart, numerically facile people who glibly say they support the science of anthropogenic global warming would be appalled if they actually looked at it in any depth. While gender studies grads and journalism majors seem consistently impressed with the IPCC, physicists, economics, geologists, and others more used to a level of statistical rigor generally turn from believers to skeptics once they dig into the details. I did.