What, exactly, do warmists think that deniers are denying?

[IlarMeilyr]

It is science and it is settled.

No. It isn't.

It isn't genuine science. And it is far from "settled."

 

[Wyld Kard]

It is science and it is settled.

Once again, Dishonest Abe has proven that he is indeed FULL OF BULLSHIT!

 

[Abraham3]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

 

[elektra]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

Now you ask? I guess your still only up to "c" with that dictionary.

Its easy, anything abraHAM posts or links to is not Science.

 

[jc456]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

Science, developing theory and confirming said theory. If theory is not proven, adjust the theory so one can find confirmation. Not, ignore the data and stay with the theory. That is not science.

 

[IlarMeilyr]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

It might prove instructive and amusing to get YOU to define the term you so casually and ignorantly bandy about. I mean, seriously. It is quite clear that you lack the first damn clue as to the actual definition of "science," much less any grasp of how "science" is properly conducted.

Go.

 

[Abraham3]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

It might prove instructive and amusing to get YOU to define the term you so casually and ignorantly bandy about. I mean, seriously. It is quite clear that you lack the first damn clue as to the actual definition of "science," much less any grasp of how "science" is properly conducted.

Go.

Science is the process by which we increase our knowledge of the universe around us and its functions. Through research, investigation and analysis conducted per the methodologies laid out by Francis Bacon and further developed and validated by Newton, Galileo, Parmenides, Alhazen and others.

I think we are all familiar with observations -> hypothesis -> test -> theory / new hypothesis. Below from Wikipedia is a far better explanation than any I could write you.

The overall process involves making conjectures (hypotheses), deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions to determine whether the original conjecture was correct.[18] There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, they are better considered as general principles.[19] Not all steps take place in every scientific inquiry (or to the same degree), and are not always in the same order. As noted by William Whewell (1794–1866), "invention, sagacity, [and] genius"[20] are required at every step:

Formulation of a question: The question can refer to the explanation of a specific observation, as in "Why is the sky blue?", but can also be open-ended, as in "How can I design a drug to cure this particular disease?" This stage also involves looking up and evaluating evidence from previous experiments, personal scientific observations or assertions, and/or the work of other scientists. If the answer is already known, a different question that builds on the previous evidence can be posed. When applying the scientific method to scientific research, determining a good question can be very difficult and affects the final outcome of the investigation.[21]

Hypothesis: An hypothesis is a conjecture, based on knowledge obtained while formulating the question, that may explain the observed behavior of a part of our universe. The hypothesis might be very specific, e.g., Einstein's equivalence principle or Francis Crick's "DNA makes RNA makes protein",[22] or it might be broad, e.g., unknown species of life dwell in the unexplored depths of the oceans. A statistical hypothesis is a conjecture about some population. For example, the population might be people with a particular disease. The conjecture might be that a new drug will cure the disease in some of those people. Terms commonly associated with statistical hypotheses are null hypothesis and alternative hypothesis. A null hypothesis is the conjecture that the statistical hypothesis is false, e.g., that the new drug does nothing and that any cures are due to chance effects. Researchers normally want to show that the null hypothesis is false. The alternative hypothesis is the desired outcome, e.g., that the drug does better than chance. A final point: a scientific hypothesis must be falsifiable, meaning that one can identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, it cannot be meaningfully tested.

Prediction: This step involves determining the logical consequences of the hypothesis. One or more predictions are then selected for further testing. The less likely that the prediction would be correct simply by coincidence, the stronger evidence it would be if the prediction were fulfilled; evidence is also stronger if the answer to the prediction is not already known, due to the effects of hindsight bias (see also postdiction). Ideally, the prediction must also distinguish the hypothesis from likely alternatives; if two hypotheses make the same prediction, observing the prediction to be correct is not evidence for either one over the other. (These statements about the relative strength of evidence can be mathematically derived using Bayes' Theorem.)

Testing: This is an investigation of whether the real world behaves as predicted by the hypothesis. Scientists (and other people) test hypotheses by conducting experiments. The purpose of an experiment is to determine whether observations of the real world agree with or conflict with the predictions derived from an hypothesis. If they agree, confidence in the hypothesis increases; otherwise, it decreases. Agreement does not assure that the hypothesis is true; future experiments may reveal problems. Karl Popper advised scientists to try to falsify hypotheses, i.e., to search for and test those experiments that seem most doubtful. Large numbers of successful confirmations are not convincing if they arise from experiments that avoid risk.[23] Experiments should be designed to minimize possible errors, especially through the use of appropriate scientific controls. For example, tests of medical treatments are commonly run as double-blind tests. Test personnel, who might unwittingly reveal to test subjects which samples are the desired test drugs and which are placebos, are kept ignorant of which are which. Such hints can bias the responses of the test subjects. Furthermore, failure of an experiment does not necessarily mean the hypothesis is false. Experiments always depend on several hypotheses, e.g., that the test equipment is working properly, and a failure may be a failure of one of the auxiliary hypotheses. (See the Duhem-Quine thesis.) Experiments can be conducted in a college lab, on a kitchen table, at CERN's Large Hadron Collider, at the bottom of an ocean, on Mars (using one of the working rovers), and so on. Astronomers do experiments, searching for planets around distant stars. Finally, most individual experiments address highly specific topics for reasons of practicality. As a result, evidence about broader topics is usually accumulated gradually.

Analysis: This involves determining what the results of the experiment show and deciding on the next actions to take. The predictions of the hypothesis are compared to those of the null hypothesis, to determine which is better able to explain the data. In cases where an experiment is repeated many times, a statistical analysis such as a chi-squared test may be required. If the evidence has falsified the hypothesis, a new hypothesis is required; if the experiment supports the hypothesis but the evidence is not strong enough for high confidence, other predictions from the hypothesis must be tested. Once a hypothesis is strongly supported by evidence, a new question can be asked to provide further insight on the same topic. Evidence from other scientists and experience are frequently incorporated at any stage in the process. Depending on the complexity of the experiment, many iterations may be required to gather sufficient evidence to answer a question with confidence, or to build up many answers to highly specific questions in order to answer a single broader question.

This model underlies the scientific revolution.[24] One thousand years ago, Alhazen demonstrated the importance of forming questions and subsequently testing them,[25] an approach which was advocated by Galileo in 1638 with the publication of Two New Sciences.[26] The current method is based on a hypothetico-deductive model[27] formulated in the 20th century, although it has undergone significant revision since first proposed.
******************************************************************
And that's what makes good science.

 

[elektra]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

It might prove instructive and amusing to get YOU to define the term you so casually and ignorantly bandy about. I mean, seriously. It is quite clear that you lack the first damn clue as to the actual definition of "science," much less any grasp of how "science" is properly conducted.

Go.

Science is the process by which we increase our knowledge of the universe around us and its functions. Through research, investigation and analysis conducted per the methodologies laid out by Francis Bacon and further developed and validated by Newton, Galileo, Parmenides, Alhazen and others.

I think we are all familiar with observations -> hypothesis -> test -> theory / new hypothesis. Below from Wikipedia is a far better explanation than any I could write you.

The overall process involves making conjectures (hypotheses), deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions to determine whether the original conjecture was correct.[18] There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, they are better considered as general principles.[19] Not all steps take place in every scientific inquiry (or to the same degree), and are not always in the same order. As noted by William Whewell (1794–1866), "invention, sagacity, [and] genius"[20] are required at every step:

Formulation of a question: The question can refer to the explanation of a specific observation, as in "Why is the sky blue?", but can also be open-ended, as in "How can I design a drug to cure this particular disease?" This stage also involves looking up and evaluating evidence from previous experiments, personal scientific observations or assertions, and/or the work of other scientists. If the answer is already known, a different question that builds on the previous evidence can be posed. When applying the scientific method to scientific research, determining a good question can be very difficult and affects the final outcome of the investigation.[21]

Hypothesis: An hypothesis is a conjecture, based on knowledge obtained while formulating the question, that may explain the observed behavior of a part of our universe. The hypothesis might be very specific, e.g., Einstein's equivalence principle or Francis Crick's "DNA makes RNA makes protein",[22] or it might be broad, e.g., unknown species of life dwell in the unexplored depths of the oceans. A statistical hypothesis is a conjecture about some population. For example, the population might be people with a particular disease. The conjecture might be that a new drug will cure the disease in some of those people. Terms commonly associated with statistical hypotheses are null hypothesis and alternative hypothesis. A null hypothesis is the conjecture that the statistical hypothesis is false, e.g., that the new drug does nothing and that any cures are due to chance effects. Researchers normally want to show that the null hypothesis is false. The alternative hypothesis is the desired outcome, e.g., that the drug does better than chance. A final point: a scientific hypothesis must be falsifiable, meaning that one can identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, it cannot be meaningfully tested.

Prediction: This step involves determining the logical consequences of the hypothesis. One or more predictions are then selected for further testing. The less likely that the prediction would be correct simply by coincidence, the stronger evidence it would be if the prediction were fulfilled; evidence is also stronger if the answer to the prediction is not already known, due to the effects of hindsight bias (see also postdiction). Ideally, the prediction must also distinguish the hypothesis from likely alternatives; if two hypotheses make the same prediction, observing the prediction to be correct is not evidence for either one over the other. (These statements about the relative strength of evidence can be mathematically derived using Bayes' Theorem.)

Testing: This is an investigation of whether the real world behaves as predicted by the hypothesis. Scientists (and other people) test hypotheses by conducting experiments. The purpose of an experiment is to determine whether observations of the real world agree with or conflict with the predictions derived from an hypothesis. If they agree, confidence in the hypothesis increases; otherwise, it decreases. Agreement does not assure that the hypothesis is true; future experiments may reveal problems. Karl Popper advised scientists to try to falsify hypotheses, i.e., to search for and test those experiments that seem most doubtful. Large numbers of successful confirmations are not convincing if they arise from experiments that avoid risk.[23] Experiments should be designed to minimize possible errors, especially through the use of appropriate scientific controls. For example, tests of medical treatments are commonly run as double-blind tests. Test personnel, who might unwittingly reveal to test subjects which samples are the desired test drugs and which are placebos, are kept ignorant of which are which. Such hints can bias the responses of the test subjects. Furthermore, failure of an experiment does not necessarily mean the hypothesis is false. Experiments always depend on several hypotheses, e.g., that the test equipment is working properly, and a failure may be a failure of one of the auxiliary hypotheses. (See the Duhem-Quine thesis.) Experiments can be conducted in a college lab, on a kitchen table, at CERN's Large Hadron Collider, at the bottom of an ocean, on Mars (using one of the working rovers), and so on. Astronomers do experiments, searching for planets around distant stars. Finally, most individual experiments address highly specific topics for reasons of practicality. As a result, evidence about broader topics is usually accumulated gradually.

Analysis: This involves determining what the results of the experiment show and deciding on the next actions to take. The predictions of the hypothesis are compared to those of the null hypothesis, to determine which is better able to explain the data. In cases where an experiment is repeated many times, a statistical analysis such as a chi-squared test may be required. If the evidence has falsified the hypothesis, a new hypothesis is required; if the experiment supports the hypothesis but the evidence is not strong enough for high confidence, other predictions from the hypothesis must be tested. Once a hypothesis is strongly supported by evidence, a new question can be asked to provide further insight on the same topic. Evidence from other scientists and experience are frequently incorporated at any stage in the process. Depending on the complexity of the experiment, many iterations may be required to gather sufficient evidence to answer a question with confidence, or to build up many answers to highly specific questions in order to answer a single broader question.

This model underlies the scientific revolution.[24] One thousand years ago, Alhazen demonstrated the importance of forming questions and subsequently testing them,[25] an approach which was advocated by Galileo in 1638 with the publication of Two New Sciences.[26] The current method is based on a hypothetico-deductive model[27] formulated in the 20th century, although it has undergone significant revision since first proposed.
******************************************************************
And that's what makes good science.

The Moron goes to Wikipedia to figure out what Science is, abraHAM can not even define Science in his own words.

 

[Abraham3]

Science is the process by which we increase our knowledge of the universe around us and its functions. Through research, investigation and analysis conducted per the methodologies laid out by Francis Bacon and further developed and validated by Newton, Galileo, Parmenides, Alhazen and others.

I think we are all familiar with observations -> hypothesis -> test -> theory / new hypothesis. Below from Wikipedia is a far better explanation than any I could write you.


[snip]

And that's what makes good science.

The Moron goes to Wikipedia to figure out what Science is, abraHAM can not even define Science in his own words.

The words above are mine. The point of this issue was for YOU to explain what YOU believe makes good science. Answering my question with another question, as Ilar Meilyr did here, is just a blunt and ignorant manner to avoid answering a question.

Do any of YOU have anything to add about what makes good science? Given the nature of many of the comments made here, I assumed you would want to explain how climate scientists have all been violating these guidelines and procedures; give examples, name names, that sort of thing.

 

[Abraham3]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

Science, developing theory and confirming said theory. If theory is not proven, adjust the theory so one can find confirmation. Not, ignore the data and stay with the theory. That is not science.

What data do you believe is being ignored?

To what theory do you believe people are clinging despite the above-mentioned data?

Who is doing this?

 

[jc456]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

Science, developing theory and confirming said theory. If theory is not proven, adjust the theory so one can find confirmation. Not, ignore the data and stay with the theory. That is not science.

What data do you believe is being ignored?

To what theory do you believe people are clinging despite the above-mentioned data?

Who is doing this?

Wow, have you been following the threads in the forum? Really, you have no idea what theories or models are being ignored although data doesn't support them? One quick one is the IPCC AR5 report, read it and you will find in there where they conclude the latest slowing of warming (LOL) and even though, they stand behind their AR4 models. Huh? They're not asking the science groups to go back and figure out why the models were in error. Nope still have high confidence in a model that was wrong. That isn't science. again, I didn't see your explanation of what science was. You went to wikipedia. Nice. Seems a bit hypocritical. But hey, what did I expect.

 

[elektra]

Scientist, a term thrown around a little too easy.

Too many people get the title, Scientist. What do they do for us? Cost us a shitload more money! Seriously are they working to make the USA the strongest nation in the world by giving us cheap power or do they work against that.

We get nothing but endless ridiculousness in the form of, a "study".

I guarantee the average idiot off the street is at least as smart as the, "below average scientists".

Scientist, I bet less then 1% in the field of Science that call themselves, Scientist, deserve the title.

More like imbeciles that never left school, people who never worked, never left the university, they are so disconnected to life and the earth I bet you a 100 bucks if they went to the Antarctic they could not even tell if its cold enough to freeze an ocean around their ship.

morons

 

[IanC]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

Science, developing theory and confirming said theory. If theory is not proven, adjust the theory so one can find confirmation. Not, ignore the data and stay with the theory. That is not science.

What data do you believe is being ignored?

To what theory do you believe people are clinging despite the above-mentioned data?

Who is doing this?

here is a quote from Samuel Clements directly to you-

“There is something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment of fact.” ― Mark Twain

it is the conjecture that deniers are denying, not the trifling facts.

 

[jc456]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

Science, developing theory and confirming said theory. If theory is not proven, adjust the theory so one can find confirmation. Not, ignore the data and stay with the theory. That is not science.

What data do you believe is being ignored?

To what theory do you believe people are clinging despite the above-mentioned data?

Who is doing this?

Yo friend, I'm still waiting for your definition of science. Please enlighten us all oh wise one!!!

 

[jc456]

Okay folks, how do YOU tell what is and is not science? Let's hear it.

It might prove instructive and amusing to get YOU to define the term you so casually and ignorantly bandy about. I mean, seriously. It is quite clear that you lack the first damn clue as to the actual definition of "science," much less any grasp of how "science" is properly conducted.

Go.

Science is the process by which we increase our knowledge of the universe around us and its functions. Through research, investigation and analysis conducted per the methodologies laid out by Francis Bacon and further developed and validated by Newton, Galileo, Parmenides, Alhazen and others.

I think we are all familiar with observations -> hypothesis -> test -> theory / new hypothesis. Below from Wikipedia is a far better explanation than any I could write you.

The overall process involves making conjectures (hypotheses), deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions to determine whether the original conjecture was correct.[18] There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, they are better considered as general principles.[19] Not all steps take place in every scientific inquiry (or to the same degree), and are not always in the same order. As noted by William Whewell (1794–1866), "invention, sagacity, [and] genius"[20] are required at every step:

Formulation of a question: The question can refer to the explanation of a specific observation, as in "Why is the sky blue?", but can also be open-ended, as in "How can I design a drug to cure this particular disease?" This stage also involves looking up and evaluating evidence from previous experiments, personal scientific observations or assertions, and/or the work of other scientists. If the answer is already known, a different question that builds on the previous evidence can be posed. When applying the scientific method to scientific research, determining a good question can be very difficult and affects the final outcome of the investigation.[21]

Hypothesis: An hypothesis is a conjecture, based on knowledge obtained while formulating the question, that may explain the observed behavior of a part of our universe. The hypothesis might be very specific, e.g., Einstein's equivalence principle or Francis Crick's "DNA makes RNA makes protein",[22] or it might be broad, e.g., unknown species of life dwell in the unexplored depths of the oceans. A statistical hypothesis is a conjecture about some population. For example, the population might be people with a particular disease. The conjecture might be that a new drug will cure the disease in some of those people. Terms commonly associated with statistical hypotheses are null hypothesis and alternative hypothesis. A null hypothesis is the conjecture that the statistical hypothesis is false, e.g., that the new drug does nothing and that any cures are due to chance effects. Researchers normally want to show that the null hypothesis is false. The alternative hypothesis is the desired outcome, e.g., that the drug does better than chance. A final point: a scientific hypothesis must be falsifiable, meaning that one can identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, it cannot be meaningfully tested.

Prediction: This step involves determining the logical consequences of the hypothesis. One or more predictions are then selected for further testing. The less likely that the prediction would be correct simply by coincidence, the stronger evidence it would be if the prediction were fulfilled; evidence is also stronger if the answer to the prediction is not already known, due to the effects of hindsight bias (see also postdiction). Ideally, the prediction must also distinguish the hypothesis from likely alternatives; if two hypotheses make the same prediction, observing the prediction to be correct is not evidence for either one over the other. (These statements about the relative strength of evidence can be mathematically derived using Bayes' Theorem.)

Testing: This is an investigation of whether the real world behaves as predicted by the hypothesis. Scientists (and other people) test hypotheses by conducting experiments. The purpose of an experiment is to determine whether observations of the real world agree with or conflict with the predictions derived from an hypothesis. If they agree, confidence in the hypothesis increases; otherwise, it decreases. Agreement does not assure that the hypothesis is true; future experiments may reveal problems. Karl Popper advised scientists to try to falsify hypotheses, i.e., to search for and test those experiments that seem most doubtful. Large numbers of successful confirmations are not convincing if they arise from experiments that avoid risk.[23] Experiments should be designed to minimize possible errors, especially through the use of appropriate scientific controls. For example, tests of medical treatments are commonly run as double-blind tests. Test personnel, who might unwittingly reveal to test subjects which samples are the desired test drugs and which are placebos, are kept ignorant of which are which. Such hints can bias the responses of the test subjects. Furthermore, failure of an experiment does not necessarily mean the hypothesis is false. Experiments always depend on several hypotheses, e.g., that the test equipment is working properly, and a failure may be a failure of one of the auxiliary hypotheses. (See the Duhem-Quine thesis.) Experiments can be conducted in a college lab, on a kitchen table, at CERN's Large Hadron Collider, at the bottom of an ocean, on Mars (using one of the working rovers), and so on. Astronomers do experiments, searching for planets around distant stars. Finally, most individual experiments address highly specific topics for reasons of practicality. As a result, evidence about broader topics is usually accumulated gradually.

Analysis: This involves determining what the results of the experiment show and deciding on the next actions to take. The predictions of the hypothesis are compared to those of the null hypothesis, to determine which is better able to explain the data. In cases where an experiment is repeated many times, a statistical analysis such as a chi-squared test may be required. If the evidence has falsified the hypothesis, a new hypothesis is required; if the experiment supports the hypothesis but the evidence is not strong enough for high confidence, other predictions from the hypothesis must be tested. Once a hypothesis is strongly supported by evidence, a new question can be asked to provide further insight on the same topic. Evidence from other scientists and experience are frequently incorporated at any stage in the process. Depending on the complexity of the experiment, many iterations may be required to gather sufficient evidence to answer a question with confidence, or to build up many answers to highly specific questions in order to answer a single broader question.

This model underlies the scientific revolution.[24] One thousand years ago, Alhazen demonstrated the importance of forming questions and subsequently testing them,[25] an approach which was advocated by Galileo in 1638 with the publication of Two New Sciences.[26] The current method is based on a hypothetico-deductive model[27] formulated in the 20th century, although it has undergone significant revision since first proposed.
******************************************************************
And that's what makes good science.

In case there are any quesitons which direction AGW is headed.
UN Scientists Who Have Turned on the UN IPCC & Man-Made Climate Fears ? A Climate Depot Flashback Report | Climate Depot