Killing people is the best solution.

Brandon9000 wrote:

The law is primarily aimed at the subset of murderers who are not completely oblivious to consequences.

Those potential murders that consider consequences: are they really deterred that much more by the threat of capital punishment than they are by life in prison without parole?

It is reasonable to believe that until you are presented with research that shows the opposite. Then it is reasonable to at least suspend said belief. It seems that for some, winning is more important than learning.

Here's another study that debunks your deterrant hypothesis:

Capital Punishment and Deterrence: Examining the Effect of Executions on Murder in Texas

Quote:

This study tested the deterrence hypothesis in Texas, the most active execution jurisdiction during the modern era. Using monthly observations during 1984 through 1997, both the general relationship between executions and murder rates and the specific relationship between executions and felony murder rates were examined. An initial bivariate relationship between executions and murder rates proved to be spurious when appropriate control variables were included in regression models. Within a context so ideally suited for finding any potential deterrent effects, this study confirmed the results of previous ones that failed to find any evidence of deterrence resulting from capital punishment.

Correlation does not imply causation is a phrase used in the sciences and the statistics to emphasize that correlation between two variables does not imply that one causes the other. Its negation, correlation proves causation, is a logical fallacy by which two events that occur together are claimed to have a cause-and-effect relationship. The fallacy is also known as cum hoc ergo propter hoc (Latin for "with this, therefore because of this") and false cause. By contrast, the fallacy post hoc ergo propter hoc requires that one event occurs before the other and so may be considered a type of cum hoc.

In a widely-studied example, numerous epidemiological studies showed that women who were taking combined hormone replacement therapy (HRT) also had a lower-than-average incidence of coronary heart disease (CHD), leading doctors to propose that HRT was protective against CHD. But controlled trials showed that HRT caused a small and significant increase in risk of CHD. Re-analysis of the data showed that women undertaking HRT were more likely to be from socio-economic groups ABC1, with better than average diet and exercise regimes. The two were coincident effects of a common cause, rather than cause and effect as had been supposed.[1]

...

Usage
In the strictest sense, it is always correct to say "Correlation does not imply causation". However, the word "imply" in casual use loosely means suggests rather than requires. The idea that correlation and causation are connected is certainly true; correlation is needed for causation to be proved.

...

General pattern
The cum hoc ergo propter hoc logical fallacy can be expressed as follows:

A occurs in correlation with B.
Therefore, A causes B.

In this type of logical fallacy, one makes a premature conclusion about causality after observing only a correlation between two or more factors. Generally, if one factor (A) is observed to only be correlated with another factor (B), it is sometimes taken for granted that A is causing B even when no evidence supports this. This is a logical fallacy because there are at least four other possibilities:

1. B may be the cause of A, or
2. some unknown third factor is actually the cause of the relationship between A and B, or
3. the "relationship" is so complex it can be labelled coincidental (i.e., two events occurring at the same time that have no simple relationship to each other besides the fact that they are occurring at the same time).
4. B may be the cause of A at the same time as A is the cause of B (contradicting that the only relationship between A and B is that A causes B). This describes a self-reinforcing system.

In other words, there can be no conclusion made regarding the existence or the direction of a cause and effect relationship only from the fact that A and B are correlated. Determining whether there is an actual cause and effect relationship requires further investigation, even when the relationship between A and B is statistically significant, a large effect size is observed, or a large part of the variance is explained.

...

They have a little discussion about the difference between "imply" when it is used in science and "imply" when it is used in casual discussions, which may forgive your confusion. It does not forgive your churlish behavior, though.

Watch: Smoking causes lung cancer. This is true, right? How do we know? We know because we've observed mountains of data that implies those who smoke have a greater chance of being stricken with lung cancer. This has never been directly observed. There has never been a perfectly controlled environment to observe it. We also know that some non-smokers get lung cancer and many smokers never do. However, the correlation implies causation so strongly that it has become widely accepted as fact. How would you explain this with your text-book repetition of the unfortunate phrase?

In other words, there can be no conclusion made regarding the existence or the direction of a cause and effect relationship only from the fact that A and B are correlated. Determining whether there is an actual cause and effect relationship requires further investigation, even when the relationship between A and B is statistically significant, a large effect size is observed, or a large part of the variance is explained.

Correlation and causality
Main article: Correlation does not imply causation
The conventional dictum that "correlation does not imply causation" means that correlation cannot be validly used to infer a causal relationship between the variables. This dictum should not be taken to mean that correlations cannot indicate causal relations. However, the causes underlying the correlation, if any, may be indirect and unknown. Consequently, establishing a correlation between two variables is not a sufficient condition to establish a causal relationship (in either direction).

A correlation between age and height in children is fairly causally transparent, but a correlation between mood and health in people is less so. Does improved mood lead to improved health; or does good health lead to good mood; or both? Or does some other factor underlie both? Or is it pure coincidence? In other words, a correlation can be taken as evidence for a possible causal relationship, but cannot indicate what the causal relationship, if any, might be.

Examples

Sleeping with one's shoes on is strongly correlated with waking up with a headache.
Therefore, sleeping with one's shoes on causes headache.

The above example commits the correlation-implies-causation fallacy, as it prematurely concludes that sleeping with one's shoes on causes headache. A more plausible explanation is that both are caused by a third factor, in this case alcohol intoxication, which thereby gives rise to a correlation. Thus, this is a case of possibility (2) above.




The more firemen fighting a fire, the more damage there is going to be.
Therefore firemen cause damage.

The above example is simple and easy to understand. The strong correlation between the number of firemen at a scene and the damage that is caused does not imply that the firemen cause the damage. Firemen are sent according to the severity of the fire and if there is a large fire, a greater number of firemen are sent. Large fires cause more damage.

With a decrease in the number of pirates we have seen an increase in global warming over the same time period.
Therefore, global warming is caused by a lack of pirates.

The example above is used satirically by the parody religion Pastafarianism to illustrate the logical fallacy of assuming that correlation equals causation.




Young children who sleep with the light on are much more likely to develop myopia in later life.

The former is a recent scientific example that resulted from a study at the University of Pennsylvania Medical Center. Published in the May 13, 1999 issue of Nature, the study received much coverage at the time in the popular press [3]. However, a later study at The Ohio State University did not find a link between infants sleeping with the light on and development of myopia. It did find a strong link between parental myopia and the development of child myopia, also noting that myopic parents were more likely to leave a light on in their children's bedroom [4]. This is a case of (2).




Another example:

Since the 1950s, both the atmospheric CO2 level and crime levels have increased sharply.
Hence, atmospheric CO2 causes crime.

The above example arguably makes the mistake of prematurely concluding a causal relationship where the relationship between the variables, if any, is so complex it may be labelled coincidental. The two events have no simple relationship to each other beside the fact that they are occurring at the same time. This is a case of possibility (3) above; another such example is the hoax Mierscheid Law.




As ice cream sales increase, the rate of drowning deaths increases sharply.
Therefore, ice cream causes drowning.

The aforementioned example fails to recognize the importance of time in relationship to ice cream sales. Ice cream is sold during the summer months at a much greater rate, and it is during the summer months that people are more likely to engage in activities involving water, such as swimming. The increased drowning deaths are simply caused by more exposure to water based activities, not ice cream.





Increased pressure results in increased temperature
Therefore pressure causes temperature.

The ideal gas law, PV=mRT describes the direct relationship between pressure and temperature (along with other factors) to show that there is a direct correlation between the two properties. Given a fixed mass, an increase in temperature will cause an increase in pressure, likewise, increased pressure will cause an increase in temperature. This demonstrates (4) in that the two are directly proportional to each other and not independent functions.

Watch: Smoking causes lung cancer. This is true, right? How do we know? We know because we've observed mountains of data that implies those who smoke have a greater chance of being stricken with lung cancer. This has never been directly observed. There has never been a perfectly controlled environment to observe it. We also know that some non-smokers get lung cancer and many smokers never do. However, the correlation implies causation so strongly that it has become widely accepted as fact. How would you explain this with your text-book repetition of the unfortunate phrase?

OCCOM BILL wrote:

Watch: Smoking causes lung cancer. This is true, right? How do we know? We know because we've observed mountains of data that implies those who smoke have a greater chance of being stricken with lung cancer. This has never been directly observed. There has never been a perfectly controlled environment to observe it. We also know that some non-smokers get lung cancer and many smokers never do. However, the correlation implies causation so strongly that it has become widely accepted as fact. How would you explain this with your text-book repetition of the unfortunate phrase?

The found a correlation, investigated, and found an actual causal relationship.

Correlation offers suggestions for further research. Correlation, by itself, is not enough to prove cause-and-effect.

What part of this is difficult to understand?

People have researched the possibility of cause-and-effect with regards to the death penalty deterring murder. They have found no such cause-and-effect. You may continue to delude yourself that you are right and everyone else is wrong, but I think the rest of us will just go with the preponderance of evidence, mkay?

Cop out alert! That's exactly what I said from the beginning! You have repeatedly insisted that the graph didn't show correlation. Are you now trying to back door your concession that it does?

Disclaimer: I still do not agree that you have managed to demonstrate a link between executions and the murder rate.

You are the idiot who sometimes can see the correlation, sometimes can't; depending on what FACT you are trying to deny.

Bill, believe what you want.

Let me make this point clear, however: Your graph does not demonstrate correlation.

Smoking causes Lung Cancer. This was proven almost exclusively by the correlation between smoking and Lung Cancer. Since there is no direct proof whatsoever; only an idiot would pretend this isn't so. You, sir, are that idiot.

http://able2know.org/topic/125257-5#post-3470719

DrewDad wrote:

Bill, believe what you want.

Let me make this point clear, however: Your graph does not demonstrate correlation.

How can the correlation not prove causation if no correlation exists? Are you admitting this obvious error or aren't you?

(calling me a twit doesn’t erase your contradictions)

How can the correlation not prove causation if no correlation exists?

OCCOM BILL wrote:

http://able2know.org/topic/125257-5#post-3470719

DrewDad wrote:

Bill, believe what you want.

Let me make this point clear, however: Your graph does not demonstrate correlation.

How can the correlation not prove causation if no correlation exists? Are you admitting this obvious error or aren't you?

(calling me a twit doesn’t erase your contradictions)

Try to track this Bill:

1. You have not established a correlation between enforcing the death penalty and murder rates. (I have been consistent on this point.)

2. If you manage to do so, correlation by itself is not sufficient to show that the death penalty is an effective deterrent. (I have been consistent on this point as well.)

Are you not able to keep these two arguments in your head at the same time?

I don't understand how you can pretend the obvious correlation isn't there. I don't understand how you can STILL be pretending this, while trying to make the argument that the correlation that is there doesn't prove causation. I don't understand why you think you can move the goal posts from "suggests causation" to "proves causation" without anyone noticing. You are a VERY dishonest man.

OCCOM BILL wrote:

How can the correlation not prove causation if no correlation exists?

Does this sentence make sense to you?

Demonstration of correlation is required before cause-and-effect can be established.