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This seems to be the referenced analysis: >https://www.worldweatherattribution.org/human-contribution-to-the-record-breaking-july-2019-heat-wave-in-western-eur

This seems to be the referenced analysis: >https://www.worldweatherattribution.org/human-contribution-to-the-record-breaking-july-2019-heat-wave-in-western-europe/ https://www.worldweatherattribution.org/human-contribution-t... The researchers came to their conclusion after running a multitude of climate simulations, however, this part concerns me: >Models that did not represent heat waves well were withdrawn from the analysis. Serious question: is climate science immune from the statistical abuses that have recently been shown to plague other empirical and/or model based disciplines?

robenkleene 7y ago
> Serious question: is climate science immune from the statistical abuses that have recently been shown to plague other empirical and/or model based disciplines? I'm not sure what you're referring to, and I'd love to read up on it if you have a source?
- fromthestart 7y ago
  These kinds of stories have been popping up on HN with seemingly increasingly frequency over the last couple years, e.g.: https://news.ycombinator.com/item?id=19445827 https://news.ycombinator.com/item?id=19445827 https://news.ycombinator.com/item?id=16916145 https://news.ycombinator.com/item?id=16916145 https://news.ycombinator.com/item?id=20585391 https://news.ycombinator.com/item?id=20585391 Long story short: most scientific disciplines are in crisis in part because of pervasive p-value hacking, and other statistics abuses likely motivated by perverse academic/personal incentives and/or biases.
  - robenkleene 7y ago
    Taking a look, thanks for sharing!
scarmig 7y ago
Why would it be? I wouldn't put too much stock on the exact predicted delta, but more on the direction and rough order of magnitude of the change.
Corazoor 7y ago
Not immune, but pretty reistant, yes. The results of these models are usually compared with actual data from the real world, very much like in astronomy or geology. If a model doesn't agree with reality, it gets discarded pretty quickly. In fact, that is what the researchers did in this case: They applied several statistical models of weather extremes to both models and real data and compared them. Every model that failed to predict the actual measured heat wave went out. That seems like a good way of establishing a baseline, and gives high confidence in the predictive power of the remaining models. You could argue about how the weather data was selected, and how it was compared with the models. But that is already a discussion about details of the specific study. This one is a collaboration between multiple reputable institutions, so it is pretty safe to assume sound practices. Notably, the fact that you actually know about statistical abuse in science shows that the scientific process is working as intended: Results are public and therefore can be and are scrutinized. More important stuff also receives more scrutiny, less important stuff can go unchecked until it is cited more often. But eventually errors will be unearthed and accounted for.
- microcolonel 7y ago
  > In fact, that is what the researchers did in this case: They applied several statistical models of weather extremes to both models and real data and compared them. Every model that failed to predict the actual measured heat wave went out. Wouldn't it be better to select the models which have had the most accurate predictions over as long a period as the required data are available? Mean squared error of temperature, or something. Promoting models primarily for their ability to predict an event that is currently happening seems like a terrible idea.
briantakita 7y ago
> Serious question: is climate science immune from the statistical abuses that have recently been shown to plague other empirical and/or model based disciplines? Here is a skeptic's analysis of the NOAA & IPCC data. https://www.youtube.com/watch?v=7Ag3D0rjGuc https://www.youtube.com/watch?v=7Ag3D0rjGuc
pyrale 7y ago
It is standard scientific process to discard models that fail to predict actual outcomes.

robenkleene 7y ago

> Serious question: is climate science immune from the statistical abuses that have recently been shown to plague other empirical and/or model based disciplines? I'm not sure what you're referring to, and I'd love to read up on it if you have a source?

fromthestart 7y ago
These kinds of stories have been popping up on HN with seemingly increasingly frequency over the last couple years, e.g.: https://news.ycombinator.com/item?id=19445827 https://news.ycombinator.com/item?id=19445827 https://news.ycombinator.com/item?id=16916145 https://news.ycombinator.com/item?id=16916145 https://news.ycombinator.com/item?id=20585391 https://news.ycombinator.com/item?id=20585391 Long story short: most scientific disciplines are in crisis in part because of pervasive p-value hacking, and other statistics abuses likely motivated by perverse academic/personal incentives and/or biases.
- robenkleene 7y ago
  Taking a look, thanks for sharing!

scarmig 7y ago

Why would it be? I wouldn't put too much stock on the exact predicted delta, but more on the direction and rough order of magnitude of the change.

Corazoor 7y ago

Not immune, but pretty reistant, yes. The results of these models are usually compared with actual data from the real world, very much like in astronomy or geology. If a model doesn't agree with reality, it gets discarded pretty quickly. In fact, that is what the researchers did in this case: They applied several statistical models of weather extremes to both models and real data and compared them. Every model that failed to predict the actual measured heat wave went out. That seems like a good way of establishing a baseline, and gives high confidence in the predictive power of the remaining models. You could argue about how the weather data was selected, and how it was compared with the models. But that is already a discussion about details of the specific study. This one is a collaboration between multiple reputable institutions, so it is pretty safe to assume sound practices. Notably, the fact that you actually know about statistical abuse in science shows that the scientific process is working as intended: Results are public and therefore can be and are scrutinized. More important stuff also receives more scrutiny, less important stuff can go unchecked until it is cited more often. But eventually errors will be unearthed and accounted for.

microcolonel 7y ago
> In fact, that is what the researchers did in this case: They applied several statistical models of weather extremes to both models and real data and compared them. Every model that failed to predict the actual measured heat wave went out. Wouldn't it be better to select the models which have had the most accurate predictions over as long a period as the required data are available? Mean squared error of temperature, or something. Promoting models primarily for their ability to predict an event that is currently happening seems like a terrible idea.

briantakita 7y ago

> Serious question: is climate science immune from the statistical abuses that have recently been shown to plague other empirical and/or model based disciplines? Here is a skeptic's analysis of the NOAA & IPCC data. https://www.youtube.com/watch?v=7Ag3D0rjGuc https://www.youtube.com/watch?v=7Ag3D0rjGuc

pyrale 7y ago

It is standard scientific process to discard models that fail to predict actual outcomes.