Seth, your persistent questions about global warming are thought-provoking, but I think you may be overlooking many other issues related to manmade pollution. These include well-documented damage to our air, land, and water, and the resultant extinction of many animal and plant species which are vital links in our food chain and bio-system. By all means, insist that global warming groups show their evidence, but don’t trivialize the overwhelming evidence of related environmental destruction.
Jim, as others have said, to focus on reducing carbon dioxide emissions when what really needs to be reduced are dangerous pollutants would be a terrible mistake. A terrible mistake that is currently being made by many people. As you say, it’s obvious that pollution is a problem. I live in Beijing part of the year! My questions about global warming will help us put our resources, our power of innovation, where they will do the most good for the environment.
Seth do you intentionally pick and choose articles which support your current view of climate change? If so, are you worried that this could bias your opinion and prevent you from taking an objective view of the situation? In other words, do you see yourself as a partisan who has a side that he wants to see win, or as an impartial observer who wishes to know the truth and is open-minded about evidence from all directions?
Hal, I pick articles that I think are revealing. It’d always possible that they reveal I’m wrong. (See the next post.) I’m not sure what it means to be “impartial” but yes I want to know the truth. I’ve been searching for evidence I’m wrong about this.
It’s a common fallacy to want to use “extreme” statistics (high/low records for example) to demonstrate trends, but the way you measure the standard error in the tail of a normal distribution is different than the way you measure standard error in changes in the mean.
The error bars are much, much bigger when you look at extreme statistics, so what might appear to be a real statistically significant change will in reality not be statistically significant (and therefore, not real).
Bottom line – don’t even bother looking at studies that use extreme measurements.
Seth, your persistent questions about global warming are thought-provoking, but I think you may be overlooking many other issues related to manmade pollution. These include well-documented damage to our air, land, and water, and the resultant extinction of many animal and plant species which are vital links in our food chain and bio-system. By all means, insist that global warming groups show their evidence, but don’t trivialize the overwhelming evidence of related environmental destruction.
Jim, as others have said, to focus on reducing carbon dioxide emissions when what really needs to be reduced are dangerous pollutants would be a terrible mistake. A terrible mistake that is currently being made by many people. As you say, it’s obvious that pollution is a problem. I live in Beijing part of the year! My questions about global warming will help us put our resources, our power of innovation, where they will do the most good for the environment.
Seth do you intentionally pick and choose articles which support your current view of climate change? If so, are you worried that this could bias your opinion and prevent you from taking an objective view of the situation? In other words, do you see yourself as a partisan who has a side that he wants to see win, or as an impartial observer who wishes to know the truth and is open-minded about evidence from all directions?
Hal, I pick articles that I think are revealing. It’d always possible that they reveal I’m wrong. (See the next post.) I’m not sure what it means to be “impartial” but yes I want to know the truth. I’ve been searching for evidence I’m wrong about this.
Re: the global warming article –
It’s a common fallacy to want to use “extreme” statistics (high/low records for example) to demonstrate trends, but the way you measure the standard error in the tail of a normal distribution is different than the way you measure standard error in changes in the mean.
The error bars are much, much bigger when you look at extreme statistics, so what might appear to be a real statistically significant change will in reality not be statistically significant (and therefore, not real).
Bottom line – don’t even bother looking at studies that use extreme measurements.
Dave