CalvinDude.com

A Timeline

Something odd happened this fall. BBC “weather presenter and climate correspondent” Paul Hudson wrote a blog article entitled Whatever happened to global warming? on October 9, 2009. Global Warming “skeptics” were shocked to see such a thing linked to BBC, of all places, and the article even made it to the Drudge Report.

Although it wouldn’t come out until November 23, just three days after posting his blog, Hudson was forwarded what he called a “chain of emails” on October 12 (source). Hudson, however, did not do anything with the files.

So on November 17, a post appeared on the Climate Audit blog stating “A miracle just happened.” It included a link to 61-MB ZIP file (unzipped, it was over 160 MB) containing thousands of “leaked” e-mails from the East Anglia Climate Research Unit (CRU). That post was quickly removed, but then a user going by the handle FOIA (for Freedom of Information Act) using an anonymous Russian FTP account posted the e-mails on the Air Vent blog.

The files sat dormant there until November 19, when another user alerted The Blackboard to its existence. This was quickly followed by a blog commentary by Anthony Watts and stories in The Examiner and in Investigate. In short, the emails had gone viral.

The next day (November 20), Phil Jones, the director of CRU, acknowledged there had been a security breach. In the process, he verified the accuracy of one e-mail in particular—the now infamous “hide the decline” email. Three days later, Paul Hudson (the CNN presenter mentioned above) stated that the leaked e-mails were identical to those he had received on October 12. The accuracy of the emails had been established so well that, to this date, I have found no indication that anyone involved has claimed any are forgeries.

Global Warming proponents have focused on the illegality of the hacking of the emails (although evidence currently points toward a CRU-insider leaking the documents due to a FOI request filed by Steve McIntyre being denied). Global Warming skeptics have focused on the contents of what was leaked. And what they show is not good for science.

Hide the Decline

One leaked e-mail was from Phil Jones, stating the following (note, typographical errors in the original):

From: Phil Jones

To: ray bradley ,mann@xxxxxxxxx.xxx, mhughes@xxxxxxxxx.xxx
Subject: Diagram for WMO Statement
Date: Tue, 16 Nov 1999 13:31:15 +0000
Cc: k.briffa@xxxxxxxxx.xxx,t.osborn@xxxxxxxxx.xxx

Dear Ray, Mike and Malcolm,
Once Tim’s got a diagram here we’ll send that either later today or
first thing tomorrow.
I’ve just completed Mike’s Nature trick of adding in the real temps
to each series for the last 20 years (ie from 1981 onwards) amd from
1961 for Keith’s to hide the decline. Mike’s series got the annual
land and marine values while the other two got April-Sept for NH land
N of 20N. The latter two are real for 1999, while the estimate for 1999
for NH combined is +0.44C wrt 61-90. The Global estimate for 1999 with
data through Oct is +0.35C cf. 0.57 for 1998.
Thanks for the comments, Ray.

Cheers
Phil

Prof. Phil Jones
Climatic Research Unit Telephone +44 (0) 1603 592090
School of Environmental Sciences Fax +44 (0) 1603 507784
University of East Anglia
Norwich Email p.jones@xxxxxxxxx.xxx
NR4 7TJ
UK

The key sentence is the one that says: “I’ve just completed Mike’s Nature trick of adding in the real temps to each series for the last 20 years (ie from 1981 onwards) amd from 1961 for Keith’s to hide the decline.”

There are a couple of things to look at. First, the date of the e-mail is November 16, 1999. Mike is Michael Mann (one of the people the e-mail was addressed to). Keith is Keith Briffa, who was cc’d on the email. The subject was “Diagram for WMO Statement” (WMO = World Meteorological Organization).

In 1999, authors of the Intergovernmental Panel on Climate Change (IPCC) met in Tanzania September 1 – 3 for the “zero-order draft” of the Third Assessment Report. Steve McIntyre got a copy of the diagram using proxy temperatures for the past 1000 years (see here for more background). The graph showed proxy temperatures gathered from Mann, Jones, and Briffa.

There was a problem with Briffa’s series. His is the hard-to-see yellow line in the excerpt below (see the above referenced link for the entire graph):

Not only is Briffa’s reconstruction lower than the others, but it’s also trending down. The IPCC was concerned, with Mann saying that this was “diluting the message” and was a “potential distraction/detraction.” So, as all good scientists do when faced with data they don’t like…

The IPCC deleted it.

That’s right. Since Briffa’s series only became problematic after 1961, they simply ended his graph at 1961. However, they hid where this cut-off occurred by burring it in the other lines from the graph (in the following, it is the green line):

Here we have scientists intentionally excluding data that didn’t fit their theory. They cherry-picked what they wanted to show, and hid that which was detrimental to their cause. Those are the actions of politicians, not scientists.

Two months after the IPCC did this, Phil Jones wrote the above email about using “Mike’s Nature trick.” Mann didn’t do the same thing that the IPCC did; his method was even more insidious. To understand it, you must first understand what is meant by proxy temperatures.

Proxies

Thermometers are a relatively new invention. Temperature scales weren’t even invented until the Eighteenth Century. In geological time, that’s less than a blink-of-the-eye ago. Even after thermometers were invented, it took a while for them to become widespread. As a result, we only have direct access to temperature recordings for roughly the past 150 years.

Scientists still like to know what the temperature was like before temperatures were recorded, but to get those numbers they have to use proxy information. This can be anything from examining ice cores in the arctic to examining tree rings (which is the method Briffa used). These ways of examining temperatures, however, are not as “fine” as a thermometer. While a thermometer can, nowadays, give you up to the second temperature readings, most proxy data has to be understood in chunks of 20-50 year periods.

As I mentioned in my earlier posts on science, precision is important for scientists. Proxy temperatures are obviously not as precise as reading temperatures off the thermometer. And when they are graphed, they need to be “smoothed” over. Yet this smoothing was not as straightforward as you might imagine it to be. As McIntyre notes:

When smoothing these time series, the Team had a problem: actual reconstructions “diverge” from the instrumental series in the last part of 20th century. For instance, in the original hockey stick (ending 1980) the last 30-40 years of data points slightly downwards. In order to smooth those time series one needs to “pad” the series beyond the end time, and no matter what method one uses, this leads to a smoothed graph pointing downwards in the end whereas the smoothed instrumental series is pointing upwards — a divergence.

What Mann did was the pad the graph with instrumental data. Now, you might be thinking that that isn’t a big deal. Mann didn’t use fake numbers; he used real temperatures gathered by instruments. However, we have to keep in mind the fact that proxy data is not as precise as instrumental data, and therefore Mann added apples to oranges to produce his graph.

Still, how important could that be? Well, when you consider that the entire blade of the famous “hockey stick” graph just is that added data, you ought to get suspicious. In fact, McIntyre and McKitrick showed that if you used “red noise” to produce random principal components data in Mann’s model, it produced the same hockey stick pattern 99% of the time. If random data creates your hockey stick, it means your model is creating the hockey stick rather than the data. (BTW, if you want to see how to make your own hockey stick model, Iowahawk has detailed instructions.)

Jones managed to take it one step further. When creating the graph for the WMO, he didn’t even bother to use the same instrument data for each series. Instead, “Mike’s series got the annual land and marine values while the other two got April-Sept for NH land N of 20N.” The result was that the graph changed from this:

To this (the actual coversheet used):

Conclusion

Mann’s hockey stick graph was the basis for much of the IPCC’s 2001 report. Mann even wrote the chapter on climate change. But despite Algore’s assurance that “the science is settled,” it is becoming more and more obvious that there is no scientific basis for man-made global warming. On the contrary, all the evidence suggests it is really Mann-made global warming.

There is much, much more to this scandal than what has simply been presented here. And it does not bode well for science in general. Science works only if scientists stick to the method and actually employ it. As soon as they begin to cherry-pick data, to mix apples and oranges, to ignore error bars, to push experiments beyond the level of precision, and to vilify those who would dare to disagree, scientists have abandoned science completely.

And the question left hanging in other people’s minds is simply this: why here? Why is it that this is the arena that scientists have chosen to forgo their scientific training and accept authoritative dictates? And more importantly, if scientists will fudge the science over this issue, what else will they be willing to fudge? If the scientific organizations who preach to us did not catch this, what else are they not catching? What other “settled science” is little more than religious dogma?

The longer scientists take to regain their credibility, the less they will be able to regain when they finally make the attempt.

Now that we have taken a look at the “ideal” description of the scientific method, it’s time to look at a more realistic (some may say “cynical”) example of how science works in the real world. It should be pointed out that most scientists genuinely do wish to adhere to the idealistic version of the scientific method. That is, they want to set up their four step loop: observation, induction, deduction, and experimentation. However, for virtually every interesting experiment, there is a big problem to getting this loop started.

Money.

It takes money to run experiments. Often, it takes massive amounts of money just to observe your experiments, especially when you deal in fields such as physics (anyone care to pay out of pocket for their own particle accelerator?). But even if you take a simple Darwinist experiment, where you observe finches in the Galapagos Islands, the scientist still must travel to the island with some kind of instruments to record data (even if just a pencil and paper), and he or she must live there with all the expenses of food and shelter that would normally be required.

So unless the scientist is independently wealthy, he relies upon OPM (Other People’s Money). And just like opium, OPM is an addictive drug. Experiments can run over-budget, and scientists are generally curious people in the first place so if there are extra funds they will find a way to put them to use (ever wonder what a loaf of bread would look like after it’s put into a vacuum?—trust me, some scientist knows). In other words, you can never get enough of OPM.

The result is that, to get an experiment done, it takes more than just the ideal four-step loop. A scientist must first start by begging for money. This is often done in the form of grant proposals. Barring that, a scientist can also decide to go work for a corporation with a vested interest in his or her field. For example, a geologist may go work for an oil company, even if he’s a vegan green New Ager, simply because the company has funds to conduct experiments loosely based on his degree; or a biologist may go work for a pharmaceutical company, even if she voted for Obama and wants universal health care coverage extended to pets, again simply because the pharmaceutical company has money to fund research. But no matter how it’s done, the scientist must first secure funding.

Funding is often extremely limited in scope and duration. But it is also simple reality that apart from a few wealthy eccentrics, the money is given to scientists as an investment. Those who give the money expect something in return for it. When it comes to corporations, obviously the owners want to increase their profits—so if you can find a cheaper way to get oil, the oil companies will fund that research; if you find medicine that prevents heart attacks, the pharmaceutical companies will fund that. For other grants, there are a few cases where people give money for “pure” science, but many come from non-profit organizations with a vested interest in proving something (to use an example I personally am aware of, although for reasons you will see later I must keep it general, a non-profit once paid an archaeology team to determine which tribe of Natives arrived at a Central American site first).

Once the scientist secures this OPM, he can then begin to do the four-step loop of “ideal” science until the funds run out. At which point, he must ask for more OPM or else the science is “done.” The net result is that scientific method ends up working like this:

1. Get OPM
2. Observe
3. Hypothesize
4. Predict
5. Experiment
6. Return to step 2 until OPM = $0.
7. Return to step 1.

Naturally, while the above loop doesn’t have an end, there will be an end to science beyond just running out of money. For instance, the pharmaceutical companies look for effective drugs, and they only do this loop until it either becomes cost prohibitive or else they succeed at finding an effective drug they can sell at profit. This opens up a new dynamic, because each experiment is finite but you can always do new experiments if you keep your nose clean.

If you have a good relationship with someone who will give you OPM, it is more likely that you will receive OPM in the future. This means that the scientist, who does not wish to spend his time getting funds—he wants to do science—is given an incentive to keep the person writing him checks happy. For corporations, this means that scientists want to keep their employer happy; for those who live by grants, they want those who give grants to continue to give those grants to them.

This puts high subjective pressure on the scientists. Science is supposed to be objective, but when scientists know they have to keep certain people happy in order to continue getting OPM just so they can do science in the first place, it’s easy to “misread” an experiment. And sometimes, it’s done intentionally. For instance, the non-profit who paid for an archaeological dig to see which Native tribe first got to a specific Central American site paid for the research because they wanted to prove a specific tribe got there before another. The archaeologists found evidence that indicated the other tribe got there first. But the published reports of the experiment said otherwise. Because scientists know which side their bread is buttered on.

(Full disclosure: obviously, this example is hearsay, but comes from a source I trust. That doesn’t mean you have to, of course.)

Scientists are no more or less human than a Wall Street stockbroker or a banker or a lawyer or your next door neighbor. They are no more likely to “do the right thing even if it costs all their funding” than would anyone else. Yes, there are some who do so (just as there are some in every profession) but there are also some who take shortcuts, fudge the data a bit, and make sure the experiments come out in the way that will benefit them.

The “corrective” for this is repeatability. Science, to be science, ought to be repeatable by others so that this can expose biases (hidden or otherwise) in the methods the scientists use.

But what happens when the same group, or a likeminded group, funds both the original experiments and the repeats? Since funding is coming from the same, or similar, source, each group of scientists has an incentive to please the person giving them OPM. And when the opposite occurs—that is, when someone who opposes the first group’s ideology funds a counter-experiment to disprove it—it is ridiculed as being biased. The net result is that science becomes politicized, and he who controls the most OPM controls the results of science.

While OPM has the most impact on the objectivity of science, it is not the only thing to impact it. In my previous post, I mentioned that science is theory-laden. That is to say, people have to have a kind of structure already in place in order to do science. The fact is that while there are a few times when people discover something serendipitously, most discoveries will occur because someone is specifically looking for something. A simple example demonstrates this. If you were asked to read a news story and then, after you finished and the story taken away, were asked “How many proper names were in that story?” you would probably have a difficult time answering; yet if you were told “I want you to read this story and tell me how many proper names are in it” before you read it, you would easily be able to keep track of that information because you are actively looking for it.

But how do you determine beforehand what information is or isn’t important in a scientific experiment? It can be quite difficult. For example, suppose you were doing an experiment into what made the best baseball player and you found that the best (however you wish to define that) baseball players tended to have been born in the summer months. How would you know that this correlation is relevant rather than accidental? This is especially insidious because one can hypothesize many different reasons why the timing of one’s birth could be relevant: babies born in the summer were gestating through winter, and perhaps the extra stress of the mother going through cold contributes someway to their development; the Earth is located at a different place in its orbit during the summer months; babies born in the summer are born in warmer weather, so maybe they start out more active; etc. All these possibilities, however, are based on the first assumption that it is actually relevant what month a player was born.

You can see from that example how it is easy to get lost down many bunny trails once dealing with information that may, or may not, be correlated to your specific experiment. What you look for and what you discard as being irrelevant depend highly upon what your theory already leads you to believe.

And what is true for the individual scientists is likewise true for groups of scientists, especially as we relate back to funding. If those who control the purse strings of your experiments are convinced that the month you’re born in determines your athletic ability, then they will not pay you if you disagree with that thesis. But since that thesis seems harmless enough—how could it really impact anything? you may wonder—then even if you don’t hold to it yourself, it’s easy enough to say you do. Soon enough, you have scientific consensus established and anyone who disagrees is, by definition, unscientific for having rejected a consensus that was derived by OPM, not science.

This makes it all the more important for scientists to be clear about the sources of their funding (to disclose possible conflicts of interest) and for us to know what effects it may have on their research. And as we shall see in my next post, this is especially true for any scientific endeavor that claims “the science is settled.”

Continuing my look into the ongoing goings on involving AGW (Alarmist Global Warming), I present the second shot in my opening salvo. Here is a slightly edited version of a paper I wrote a couple of years ago, On Scientific Method. And I think for this version, the subtitle: Why Should Steve Hays Have All The Really Long Posts on Triablogue? is apropos.

On Scientific Method

Scientists are some of the highest regarded people in Western society. Virtually everyone looks up to scientists as being discoverers and defenders of Truth (with a capital T). To a large extent, this is because as far as the public is concerned, science works. We wake up in the morning to the sounds of our alarm clocks, we get our coffee from the electrically powered pot that automatically turns on for us, and we drive cars full of sophisticated gadgetry in to our jobs, where we sit at a desk under the glow of fluorescent light bulbs and type information into a complex computer network. All of this is made possible due to the extent of technology driven by science.

It is no wonder that scientists are highly regarded then. Imagine how different the world would be if we were unable to get our coffee in the morning!

Despite the fact that scientists are so highly regarded, it is a rare individual who is actually able to determine what, by definition, science actually is. To many, the word “science” tends to bring to mind images of scrawny geeks in white lab coats playing with beakers, or possibly someone of Germanic descent sporting a wild hairdo. But these stereotypes tell us absolutely nothing about what science is.

If we were to ask someone who has just completed a scientific course, such as a high school class on biology or a college course in geology, we would get a better answer. The typical response would go something like this: Science is defined by following the scientific method [1], which begins with a scientist observing something. From this observation, the scientist makes certain predictions in the form of a hypothesis. The hypothesis is then tested via experimentation. If the results do not match the hypothesis, the scientist revises his hypothesis. This process continues until the results of the experiments confirm the hypothesis, at which point the scientist can publish his paper in a peer-reviewed journal and wait for other scientists to repeat the process. If enough scientists are able to duplicate the experiment, the hypothesis eventually becomes a well-established theory; and if the theory is confirmed over time, eventually the theory may become a scientific law.

The above process is roughly equivalent to what Moti Ben-Ari called the naïve inductive-deductive method (Ben-Ari, 2005, p. 5). In its most basic form, there are four critical steps to this method: 1. observation of something or some event; 2. induction (i.e., making a theory); 3. deduction (i.e., making predictions based off the theory); and 4. experimentation (i.e., testing the predictions to see if they conform to the theory). This four-step process ends up making a loop because the scientist observes the experiments and then uses those observations to form new theories, etc.

Naturally, the above definition requires us to define some other terms as well, the most important of which being the definition of a scientific theory. This, too, is a term that is quite often defined incorrectly by the general public. Ironically, there are some slight variations within the realm of science as well. For instance, Stephen Hawking writes:

In order to talk about the nature of the universe and to discuss questions such as whether it has a beginning or an end, you have to be clear about what a scientific theory is. I shall take the simple-minded view that a theory is just a model of the universe, or a restricted part of it, and a set of rules that relate quantities in the model to observations that we make. It exists only in our minds and does not have any other reality (whatever that might mean). A theory is a good theory if it satisfies two requirements: It must accurately describe a large class of observations on the basis of a model that contains only a few arbitrary elements, and it must make definite predictions about the results of future observations (Hawking, 1988, p. 9).

Under this definition, a scientific theory is nothing more than a mathematical model that cannot be confused with reality. This is seen even more clearly in Victor J. Stenger’s statement:

The exact relationship between the elements of scientific models and whatever true reality lies out there is not of major concern. When scientists have a model that describes the data, that is consistent with other established models, and that can be put to practical use, what else do they need? (Stenger, 2007, pp. 228-229)

Still, there are guidelines for appropriate models. The key elements to this kind of model theory is that there must be a minimum amount of arbitrary requirements for the theory, it must be widespread and not confined to a specific, ad hoc arena, and it must have the ability to make predictions.

As I said, there is some minor variation among scientists as to what constitutes a theory. An example of such a variation is:

A scientific theory is a concise and coherent set of concepts, claims, and laws (frequently expressed mathematically) that can be used to precisely and accurately explain and predict natural phenomena (Ben-Ari, 2005, p. 24).

We see in the above that this definition disagrees slightly with the definition provided by Hawking, and also comes against Stenger’s view, in that scientific theories are used to “explain and predict natural phenomena” rather than being only mathematical models that don’t need to relate to the natural world. In agreement, however, the new definition stipulates theories should be concise and coherent, which means that the theories should be short (the shorter the better) and should cohere to as much of reality as possible (with the ideal being a universal theory with absolutely no exceptions requiring arbitrary additions to the theory).

Adding to Hawking, this definition maintains that a theory should be precise and accurate. While these two terms are sometimes used synonymously in the vernacular, there is a difference between being precise and being accurate. Accuracy refers to how “correct” a theory is. This often can only be judged by viewing whether the theory successfully predicted or explained some event. Precision, on the other hand, deals with the exactness of a measurement. Suppose that the distance between a man’s house and the curb at the end of his driveway is exactly 43 feet 3 inches. If the man measures the distance using a stick that’s exactly one yard long, he gets the result of 14 yards (42 feet) plus a little bit more. If he measures the same distance with a stick that’s one foot long, he can tell what some of that “little bit more” is and gets the result of 43 feet, with just a small fraction left over now. The second measurement is more precise than the first because it is closer to the actual distance of 43 feet 3 inches, so a foot-long stick is more precise than a yard-long stick. While the first measurement was off by a foot and three inches, the second is only off by three inches.[2]

Finally, a scientific theory should be able to make explanations and/or predictions. These two concepts are actually fairly closely linked. If one is able to explain an event, one ought to be able to predict when the event will occur. However, it is important to note that simply because a theory accurately predicts an event does not mean the theory is actually true. The theory could have accidentally predicted something accurately. As Hawking notes:

Any physical theory is always provisional, in the sense that it is only a hypothesis: you can never prove it. No matter how many times the results of experiments agree with some theory, you can never be sure that the next time the result will not contradict the theory. On the other hand, you can disprove a theory by finding even a single observation that disagrees with the predictions of the theory. As philosopher of science Karl Popper has emphasized, a good theory is characterized by the fact that it makes a number of predictions that could in principle be disproved or falsified by observation. Each time new experiments are observed to agree with the predictions the theory survives, and our confidence in it is increased; but if ever a new observation is found to disagree, we have to abandon or modify the theory. At least that is what is supposed to happen, but you can always question the competence of the person who carried out the observation (Hawking, 1988, p. 10).

The provisional nature of scientific theories cannot be overstated. In fact, the history of science is filled with theories that have been discarded, from phlogiston to Ptolemy’s view of the solar system. Ironically, even some of the theories that we still use today have been shown wrong (or at least incomplete). The greatest example of this is Newton’s Law of Gravity, which was replaced by Einstein’s General Relativity, yet which is still used in physics today because Newton’s calculations are easier to handle and the discrepancies are not that great at small speeds and low mass. Still, Newton’s theory, no matter how useful, has been discarded in the ultimate sense. Science has moved on to other theories, each of them likewise held provisionally.

Unfortunately, the provisional nature of scientific theories is sometimes lost on scientists. Sometimes, a scientist can “fall in love” with his theory to such an extent that he will refuse to abandon it even after the theory has been demonstrated wrong. This result is what Thomas Kuhn documented when he showed that paradigm shifts are necessary in science. In essence, new theories do not take over until after the vast majority of practitioners with the old theories pass away. Only then do the new theories, held by new scientists who did not have the investment in the old theory, come to play. Even Einstein fell into this trap when he refused to acknowledge the validity of Quantum Mechanics despite the fact that a large portion of quantum theory came as a direct result of Einstein’s own theories!

So, to summarize what we have discussed so far, scientists begin with observations, they move on to making theories (which are concise and coherent, accurate and precise methods of explaining or predicting physical phenomena, the truth of which is held provisionally), then scientists focus on a prediction from the theory, conduct an experiment on that prediction, and observe the results, which leads us back into the loop.

All of this seems perfectly satisfactory for providing an explanation for what science is. But if you are a student of logic, you might not be as satisfied with the above. After all, the loop from observation to theory to prediction to experiment and back again seems like it could easily fall prey to circular reasoning. And indeed, this is the very reason why Ben-Moti called this method the naïve inductive-deductive method. The word “naïve” at the front of the description gives us warning to the problem.

To state the problem, let’s ask a few questions. How is it that a scientist knows what to observe in the first place? How does he know whether the experiment itself will be affected by what he is trying to discover? To use a specific example:

In 1888 when Heinrich Hertz (1857-1894) was attempting to produce the first radio waves, he did not think that the size of his lab or the color of the paint on its walls were relevant to his experiments; he knew from James Clerk Maxwell’s (1831-1879) theory of electromagnetism that radio waves were likely to exist, but he could not know that—while the color of the paint was not significant—the size of the lab was because of echoes from the walls (Ben-Ari, 2005, pp. 6-7).

It is impossible for the scientist to know these things in advance. However, he can make assumptions—and indeed, he must do so. Because of this, scientific observations are heavily theory-laden. This means that a framework for interpreting observations must exist before the scientist can begin to know if he has even observed something in the first place. But the immediate question is: how do we know if what the scientist observes is accurate anyway instead of (to use the phrase of Jack Cohen and Ian Stewart) mere “brain puns”?

The danger is that what we think of as laws may be just patterns that we somehow impose upon nature, like the animal shapes we can choose to see in clouds. Our treasured fundamental laws may just be odd features of nature that happen to appeal to the human mind. If so, then much of nature may be functioning according to processes that we cannot comprehend, and consequences derived from our imaginary laws may bear no resemblance to nature at all (Cohen & Stewart, 1994, p. 22).

So are the patterns that we profess to detect in nature brain puns or genuine laws? The verdict is not yet in, but they could be puns. In recent years a fecund mathematics has generated innumerable “new” mental images, such as catastrophes, chaos, fractals, that might be advance warning of new simplicities in the world. Each extends the list of patterns that we can name, recognize, and manipulate. It is not clear that all such patterns must necessarily prove operationally congruent to reality. They may describe games that mathematicians play, but that have nothing to do with the world outside human brains (Cohen & Stewart, 1994, p. 26).

Given only the scientific method to go with, it is impossible to know whether our observations really are reflective of reality, or if they are brain puns. Instead, we must assume that our observations are correct, which leads us into circular reasoning:

Clearly, science must start with observation, but once some initial observations have been made, a circular process takes place. Observations lead to theories, which guide further observations, which influence the theories. The presentation of the process of science as initially and primarily inductive is so oversimplified as to be useless. There are serendipitous discoveries in science, in which observations truly instigate the development of theories, but they inevitably occur to those who have the necessary framework within which to understand the importance of what they are observing (Ben-Ari, 2005, p. 8).

While Ben-Ari in the above does not dwell in great detail on the fact that science contains “a circular process” in the methodology, it is important for us now. Circular reasoning is a logical fallacy rendering arguments invalid [3]. Because of this, it might be tempting to say that the conclusions of science are illogical and therefore we shouldn’t trust the scientific method at all. However, it is important to note that all arguments assume their axioms, and therefore all arguments are equally circular at the foundational level. This is due to the fact that axioms must be assumed; they cannot be proven for if they could be proven they would not be axioms.

It is foolish, therefore, to arbitrarily decree that since the scientific method employs a degree of circularity it is completely invalid. However, the fact that the method has this circularity in it (as well as the fact that theories are always held provisionally) requires us to pause before asserting that things discovered by science are synonymous with truth. In fact, Larry Laudan maintains that science cannot actually know truth since

[t]he classical sceptic argument against science, repeated by Laudan (1984a), is that knowing the truth is a utopian task. Kant’s answer to this argument was to regard truth as a regulative principle for science. Charles S. Peirce, the founder of American pragmatism, argued that the access to the truth as the ideal limit of scientific inquiry is “destined” or guaranteed in an “indefinite” community of investigators…. However, there does not seem to be any reason to think that truth is generally accessible in this strong sense (Niiniluoto, 2007).

The upshot is that if there is no actual way to determine the truth via science then logically a scientific theory can be completely valid scientifically and yet still be false. Conversely, a theory can be completely invalid from a scientific perspective yet be true. The scientific method thus becomes all the more reliant on a supporting framework to do the “grunt work” of establishing the truth-value of science. If the framework brings us to the truth, then the circularity employed by the scientific method is harmless. But if the framework brings us to error, the fact that the scientific method is chained to this framework means that the scientific method will lead us to error every single time it is used. Naturally, the question arises: what is the framework that science uses?

If you remember in the definition of a scientific theory that we provided earlier, the theory is required to be about “natural phenomena.” This provides us with the framework used by the vast majority of scientists: philosophical naturalism. Sometimes, naturalism is also referred to as materialism since both naturalism and materialism teach that only the natural (or material) is knowable; the supernatural (or immaterial) is not. While some maintain differences between naturalism and materialism, such do not concern us here and we can treat both words synonymously.

One might first be tempted to ask whether science itself requires a naturalistic or materialistic framework to rest upon. Because naturalism has been included in the very definition of a “scientific theory,” many scientists believe that it is a requirement of science. That is, they claim it is impossible for science to exist as science in any realm other than the naturalistic realm.

But simply defining naturalism into science isn’t very appealing. Furthermore, occasionally some scientists address the philosophical issues in a more realistic manner. One such scientist was Richard Lewontin who, in a review of Carl Sagan’s book The Demon-Haunted World, wrote the following:

Our willingness to accept scientific claims that are against common sense is the key to an understanding of the real struggle between science and the supernatural. We take the side of science in spite of the patent absurdity of some of its constructs, in spite of its failure to fulfill many of its extravagant promises of health and life, in spite of the tolerance of the scientific community for unsubstantiated just-so stories, because we have a prior commitment, a commitment to materialism. It is not that the methods and institutions of science somehow compel us to accept a material explanation of the phenomenal world, but, on the contrary, that we are forced by our a priori adherence to material causes to create an apparatus of investigation and a set of concepts that produce material explanations, no matter how counter-intuitive, no matter how mystifying to the uninitiated. Moreover, that materialism is absolute, for we cannot allow a Divine Foot in the door. The eminent Kant scholar Lewis Beck used to say that anyone who could believe in God could believe in anything. To appeal to an omnipotent deity is to allow that at any moment the regularities of nature may be ruptured, that miracles may happen (Lewontin, 1997).

Lewontin almost certainly regretted penning this paragraph once it was seized upon by several Christian apologetics organizations and repeated widely around the Internet. However, the validity of Lewontin’s statements cannot be denied. If we are to define science as primarily materialistic or naturalistic, it is only because our presupposed framework compels us to create science in that manner. Science itself does not require a materialistic framework. Instead, the materialistic framework creates the scientific apparatus.

It is important to keep this order in mind. If a scientist argues that science cannot deal with the supernatural, that it is limited to examining only the natural, this statement is correct but only in a trivial sense. It is limited to that extent because the framework presupposes materialism, not because the scientific method could not be extended to include supernatural frameworks too.

Naturally, Lewontin would disagree that science could extend to the supernatural, for he states quite forcefully that “anyone who could believe in God could believe in anything.” It is somewhat ironic, however, to read that line immediately after reading how one must accept the “extravagant”, “counter-intuitive”, and “mystifying” claims of materialistic science. In what sense is it that the theist is the one who “could believe in anything” when compared to this?

But Lewontin is not the only scientist to make this claim about supernaturalism. Indeed, it is common amongst many scientists (although primarily atheistic scientists) to claim that an appeal to the supernatural is an appeal to “anything.” For instance, Ben-Ari writes:

The variety of supernatural explanations is immense and they can be used to explain the occurrence of any phenomenon. A drought must have been caused by the anger of a god disillusioned with the evil actions of the residents of a region, and a disease must have been caused by the sins of the individual. The problem with supernatural explanations is that they are vacuous. A supernatural entity can be used to explain anything, both a phenomenon and its absence, so it lacks any explanatory or predictive content (Ben-Ari, 2005, p. 29).

Naturally, this is only so if one ignores the framework of each individual supernatural position. That there are a multitude of supernatural beliefs does not mean that each supernatural belief would result in the above characterization. In fact, it is most likely (given the pluralistic and omnibenevolent nature of most supernatural religious beliefs) that there are more supernatural views that would disagree with Ben-Ari’s claims that one could attribute a drought to the anger of a disillusioned god then there are supernatural views that would agree with his example. Furthermore, this characterization of the supernatural ignores the ability of a believer in the supernatural to hold to God as a “prime cause” using “secondary causes” throughout nature.

However, this particular detail of the philosophy of science is not very relevant to our current work. For the purposes of this work, we will agree for the sake of argument that science is naturalistic. This is not to agree that it actually is, of course; the agreement is simply due to the fact that there are more pressing concerns at this point.

Because science is considered naturalistic, it is important to bring up another qualification of what science is. Or in this case, what science is not. Science declares that nature is not teleological. This rather ominous looking word is actually a very simple one to define. Teleology is the study of design (literally “purpose”, from the Greek telos), and a teleological realm would be a designed realm. It is important that we remember that naturalistic science is explicitly not teleological, and this importance is not lost upon naturalistic scientists either, as demonstrated below:

Modern science explicitly and emphatically rejects teleology. Physics can describe the trajectory of a falling stone in great detail, but it never attempts to attribute desire or purpose to stones. Biology can describe the evolutionary processes that brought our species Homo sapiens onto the face of the Earth, but it has nothing to say about why we are here, nor even if our existence has any purpose whatsoever. Nevertheless, evocative teleological terminology is often used, deepening the confusion of what science is all about. For example, a biologist might say that a species has adapted to an environmental niche, implying that the species decided to adapt or strove to adapt. Of course, science claims nothing of the sort. Adaptation is simply the outcome of a process of reproduction amid competition and does not require a decision or intention on the part of any member of the species (Ben-Ari, 2005, p. 24).

Teleological explanations have also been rejected by modern science, which seeks to describe the structure and functioning of the universe, without attributing purpose or volition to natural objects (Ben-Ari, 2005, p. 29).

The rejection of teleology goes hand-in-hand with a naturalistic worldview, one that does not view the universe as having been created with a purpose or designed in any manner. It is therefore not surprising that science is so adamant in its rejection of teleological explanations. What is not quite so easy to understand, however, is why scientists continually slip into teleological claims, as we shall see later on in this work, despite their vehemence against teleology.

We now have a very stable understanding of how the scientific method is defined. However, if you read the dialogue between Achilles and Tortoise, there were two other aspects that need to be looked at. The first is the argument from authority, and the second is the argument from consensus.

Arguments from authority are often the easiest of arguments to fall in to. This occurs whenever an individual person becomes the arbiter of truth for science. A simple example of this fallacy might be to say that because Einstein rejected quantum theory, we ought to reject it too.

Science however has always been anti-authoritative. For instance, Henry Gee, a British paleontologist, wrote the following describing his attitude when he first began to conduct research:

[M]y summer work in the Fossil Fish Section often forced me, a complete beginner, to make decisions about taxonomy: I had to reclassify specimens of pteraspid fishes, renaming them according to my reading of Alain Blieck’s thesis. I had to write out new labels and shuffle the entries that each fossil had in the museum card index. On one occasion I had a crisis of confidence. What right had I, a novice who had done no serious work on fossils, to rearrange the national collection? I took my worries to Peter Forey. ‘Don’t worry about it’, he counseled: ‘taxonomy is only a matter of opinion’. The implication was that my opinion counted; it was as valid as the opinion of qualified scientists such as Patterson, Rosen, Gardiner, or Forey (Gee, 1999, p. 154).

Further, we read from Carl Sagan’s The Demon-Haunted World (the work that Lewontin reviewed):

One of the great commandments of science is, “Mistrust arguments from authority.” (Scientists, being primates, and thus given to dominance hierarchies, of course do not always follow this commandment.) Too many such arguments have proved too painfully wrong. Authorities must prove their contentions like everybody else. This independence of science, its occasional unwillingness to accept conventional wisdom, makes it dangerous to doctrines less self-critical, or with pretensions to certitude (Sagan, 1996, p. 28).

Consensus is a slightly different issue than authority, however. While every scientist will strive to reject arguments from authority (except when they “do not always follow this commandment” as Sagan points out), many scientists flock toward the idea of consensus. This is due in part to the arguments that Tortoise used: there is a statistical advantage in presenting your work to as wide a body as possible, and because individuals can err more easily than a group, consensus will tend toward the truth.

However, this concept is likewise disputed. In fact, one of the biggest problems with consensus is that it has the capability of enforcing the perceived dogmas rather than leading one toward the truth. Instead of errors being corrected, consensus can often force errors to remain firmly entrenched because it is “unscientific” to question these errors.

Michael Crichton, in a speech at Caltech, said:

Historically, the claim of consensus has been the first refuge of scoundrels; it is a way to avoid debate by claiming that the matter is already settled. Whenever you hear the consensus of scientists agrees on something or other, reach for your wallet, because you’re being had.

Let’s be clear: the work of science has nothing whatever to do with consensus. Consensus is the business of politics. Science, on the contrary, requires only one investigator who happens to be right, which means that he or she has results that are verifiable by reference to the real world. In science consensus is irrelevant. What is relevant is reproducible results. The greatest scientists in history are great precisely because they broke with the consensus.

There is no such thing as consensus science. If it’s consensus, it isn’t science. If it’s science, it isn’t consensus. Period (Crichton, 2003).

Crichton then listed some of the many times that scientific consensus has been wrong. This includes germ theory, which saved literally millions of lives but only after several hundred years of dispute before overturning the consensus, and the like. To add to Crichton, we can include the following observations by Leslie Alan Horvitz:

Something similar happened when Alfred Wegener, an astronomer and meteorologist, proposed his theory of continental drift—the idea that the continents were once all joined together and, over the eons, drifted slowly apart until they reached their present locations—was greeted with derision from geologists in the early twentieth century; today, however, his theory is the foundation of contemporary geology. Einstein’s groundbreaking theories of gravity and light also received a skeptical reception when he proposed them, but both have been confirmed repeatedly in rigorous scientific studies in the years since. Any physics textbook that doesn’t include them would be next to useless (Horvitz, 2002, pp. 4-5).

Crichton concluded the section of his speech dealing with consensus by pointing out the obvious: claims of consensus are only invoked when the science is not solid enough to stand on its own. No one argues, for example, that the sun is 93 million miles away on the basis of consensus. As Crichton says: “It would never occur to anyone to speak that way.” Furthermore, no one argues that Einstein’s claims are right due to consensus. As Horvitz pointed out above, Einstein’s theories “have been confirmed repeatedly in rigorous scientific studies.” Why appeal to consensus when you have scientific studies to fall back on?

Furthermore, to claim that science is consensus is to paint yourself into a circular corner. The consensus of scientists defines what science is, yet the science they define is what is supposed to define the scientists as scientists too. (This vicious circle was pointed out by Achilles, rendering Tortoises’ claims void.)

So while consensus sounds nice at first glance, it is indeed unnecessary to science. Science only requires adherence to reality, and that can be done by a single person even if the consensus is wrong.

Finally, suppose that we use the methods we’ve described above and we come to two competing theories that are both valid under the scientific method, yet are contradictory to each other. How do we decide between these two competing theories? Scientists have a simple rule for this sort of thing. It’s called parsimony, which is a rather non-parsimonious word to describe the process of picking the simplest theory. For example, Gee writes of cladograms:

By convention, cladists choose, as a provisional hypothesis, the most parsimonious solution: the cladogram that requires the fewest evolutionary events to support its topology—in other words, the one that assumes the smallest amount of convergence. Of course, there is no law that says that evolution is always parsimonious. However, in a world in which it is very difficult, and often impossible, to decide whether similarity reflects common ancestry or convergence, it is pragmatic to adopt solutions in which convergence is minimal and start from there. Such solutions are no more than working hypotheses, subject to test, revision—even upset—in the light of subsequent evidence (Gee, 1999, p. 185).

In light of the ongoing goings on in the wonderful world of AGW (Alarmist Global Warming), I thought it might be beneficial to remind scientists what science is supposed to consist of. As opposed to, say, alchemy. Which is when you put a bunch of random numbers in a computer program written by a failed botanist to produce hockey-stick shaped graphs before you hide the decline and throw out the raw data, because what kind of scientist could possibly look at raw data? That’s right: the kind who would write sentence fragments followed by run-on sentences switching from a declarative statement to an interrogative in the middle.

Therefore, I present…

Methodical Dialogue

TORTOISE enters ACHILLES’ room. The mythical Greek god is listening to his WalkGod CD player and is oblivious to TORTOISE.

TORTOISE: Achilles? Achilles? (He touches Achilles)

ACHILLES: Egad! What is it?

TORTOISE: Aren’t you worried you’ll ruin your hearing listening to all that noisy racket?

ACHILLES: “Noisy racket”? How can you call this noisy racket! This is none other than the Beegles compilation album, Won.

TORTOISE: Beagles?

ACHILLES: Not beagles, Beegles.

TORTOISE: It’s the same word.

ACHILLES: Almost, but not quite. Beagles are dogs. Beegles is the world’s greatest band ever.

TORTOISE: It still sounds like noise to me. In fact, maybe that’s why they’re called Beegles. They sound like braying dogs!

ACHILLES: (In disgust) They are called Beegles after the great ship HMS Beagle, which St. Darwin rode to the Galapagos Islands.

TORTOISE: St. Darwin! I know about him. He’s the patron saint of selections.

ACHILLES: The very same, only now he’s slightly more advanced.

TORTOISE: That’s somewhat handy. But why haven’t I heard any of this “Beegles” music before?

ACHILLES: Why, you probably have. You just don’t realize it. But I bet if I played a few of their tunes you would recognize them. They’re practically universal. In fact, listen to this song originally from The Scientific Misery Tour.

WALKGOD CD PLAYER: (Singing) I am the apeman, they are the apemen, I am the walnut.

ACHILLES: Surely you must know this song!

TORTOISE: Not at all.

ACHILLES: Linen would be ashamed of you.

TORTOISE: Who?

ACHILLES: The singer. But he’s dead now. (Sadly) Just like Paul.

TORTOISE: I have no idea what you are talking about.

ACHILLES: Never mind that. I’m quite sure you did not enter my room for the sole purpose of telling me that I should not listen to loud music.

TORTOISE: You are correct. I am here to propose an experiment.

ACHILLES: Hold it right there! You haven’t been talking to Zeno again, have you?

TORTOISE: Of course not.

ACHILLES: Are you sure? Sometimes he masquerades as physicist named Douglas Hofstadter.

TORTOISE: I’m positive this has nothing to do with Zeno in any alias.

ACHILLES: So this has absolutely nothing to do with one of his paradoxes?

TORTOISE: No, no. Nothing like that.

ACHILLES: Good, because last time he made me race you and I could never pass you even though I was so much faster than you are. And then he made it impossible for me to move at all because I could never get more than halfway to anywhere. It was all disconcerting for a mythical god to be bound like that.

TORTOISE: I imagine so. But this experiment is nothing like that.

ACHILLES: Okay, fine. What is your experiment?

TORTOISE: I can’t tell you.

ACHILLES: You came in here to tell me you’re going to do an experiment but you can’t tell me what it is?

TORTOISE: Indeed.

ACHILLES: Why should I care about that?

TORTOISE: Well, you’re the subject of the experiment.

ACHILLES: WHAT?!

TORTOISE: Calm down, it’s nothing preposterous.

ACHILLES: How can I trust the word of a turtle?

TORTOISE: You can’t. But I am a tortoise, not a turtle.

ACHILLES: (Scoffing) As if there’s a difference.

TORTOISE: There is a big difference! But that’s not for our current discussion.

ACHILLES: What’s to discuss? You’re conducting an experiment on me. How do I even know you’re licensed to do that?

TORTOISE: You don’t need a license to do science.

ACHILLES: Egad! They let just anyone conduct science now?

TORTOISE: Pretty much. But there are rules to it.

ACHILLES: Rules are good. Who enforces them?

TORTOISE: Scientific consensus.

ACHILLES: You take a census to determine which rules to obey?

TORTOISE: No, I said “consensus” not “census.” Silly mythical Greek god. Consensus is when a bunch of scientists get together and agree on something.

ACHILLES: I don’t know. That doesn’t sound very legit. There was a time a bunch of Persians got together and decided to attack Thermopylae, you know.

TORTOISE: True, but that was only a Persian consensus, not a scientific consensus.

ACHILLES: Oh. (Thinks about it for a minute) Wait, why does that matter?

TORTOISE: Scientific consensus is when scientists, not just Persians, get together and agree on something.

ACHILLES: I see. So no Persians are allowed.

TORTOISE: Persians are allowed, as long as they’re scientists. These scientists determine scientific consensus regardless of what ethnicity they are.

ACHILLES: So you’re saying that scientific consensus can only be determined by scientists.

TORTOISE: Indeed, I am.

ACHILLES: And scientific consensus determines who is a scientist in the first place?

TORTOISE: Again, you are correct.

ACHILLES: (Scratching his mythical chin) So scientific consensus is determined by scientists who are determined by scientific consensus, which is determined by scientists who are determined by scientific…

TORTOISE: Knock it off.

ACHILLES: Seriously, Tortoise, I think you have a problem here. It’s much better if you stick with my method.

TORTOISE: And what method is that?

ACHILLES: I am a mythical Greek god. Therefore, what I say is right.

TORTOISE: But that is an argument from authority!

ACHILLES: No less so than the authority of scientists who invent scientific consensus, I say. Besides, they’re not gods. I am.

TORTOISE: Science is not based on authority though. It’s based on consensus!

ACHILLES: Consensus is itself an authority, isn’t it?

TORTOISE: No, not at all. You’ve got it all backwards. No one person can know whether he or she is right or not. You have to have agreement between more than one person. There is no “authority” involved, because anyone can disagree with anyone else.

ACHILLES: But if they disagree with the consensus, their disagreement is by definition unscientific, isn’t it? And that means it doesn’t “count” so in what manner are they able to disagree?

TORTOISE: Look, you’re trying to make this too complicated.

ACHILLES: It is complicated. My view is much simpler, and one of the rules of science is to do that which is simplest. In my case: I said it, ergo, it’s so. You can’t get any more parsimonious than that!

TORTOISE: Egad! (Realizes what he just said) You made me use one of your words!

ACHILLES: It’s a good word.

TORTOISE: That’s irrelevant. What’s relevant is this: Each individual person has the potential to be wrong, right?

ACHILLES: “Wrong, right” has a nice ring to it.

TORTOISE: Now you’re being obtuse.

ACHILLES: It’s more fun than being abstruse.

TORTOISE: Argh, silly Greek pseudo-god. Do you agree that each individual person has the potential to be wrong?

ACHILLES: Of course. You are, ahem…”wrong right” now. See?

TORTOISE: (Ignoring the comment) If an individual person is wrong, couldn’t that error be pointed out by another person?

ACHILLES: I suppose, so long as the other person wasn’t wrong too. In that case, they would just be reinforcing the error.

TORTOISE: But isn’t it more likely that a group of people will be able to point out the errors in other people’s positions than single people working alone?

ACHILLES: “Single people”? Are you saying scientists have to be married now?

TORTOISE: Argh! You aren’t listening at all!

ACHILLES: Only because you’re not making any sense.

TORTOISE: (Fed up). Look. When you have a group of people, on the whole, the group becomes corrective. Can’t you see that a group consensus is more likely to be right than any individual’s authoritative decree?

ACHILLES: Fine. What you are saying is that you have to listen to everyone else in order to make a valid decision because you might be wrong by yourself.

TORTOISE: That’s close enough.

ACHILLES: Look around the room then. Who is in here?

TORTOISE: (Confused) Me and you. Why?

ACHILLES: You are you, and I am everyone else. Therefore, you have to listen to me.

TORTOISE: Bah, this experiment isn’t getting anywhere now.

ACHILLES: Perhaps it is because it lacks proper method?

TORTOISE glares at ACHILLES for a moment and then leaves. ACHILLES puts his WalkGod back on and presses the Play button.

WALKGOD CD PLAYER: (Singing) I am the apeman, they are the apemen, I am the walnut.