Posts Tagged ‘scientific method’

Without first having religions, atheism and agnosticism cannot exist

June 27, 2017

I take science to be the process by which areas of ignorance are explored, illuminated and then shifted into our space of knowledge. One can believe that the scientific method is powerful enough to answer all questions – eventually – by the use of our cognitive abilities. But it is nonsense to believe that science is, in itself, the answer to all questions. As the perimeter surrounding human knowledge increases, what we know that we don’t know, also increases. There is what we know and at the perimeter of what we know, lies what we don’t know. Beyond that lies the boundless space of ignorance where we don’t know what we don’t know.

Religions generally use a belief in the concept of a god (or gods) as their central tenet. By definition this is within the space of ignorance (which is where all belief lives). For some individuals the belief may be so strong that they claim it to be “personal knowledge” rather than a belief. It remains a belief though, since it cannot be proven. Buddhism takes a belief in gods to be unnecessary but – also within the space of ignorance – believes in rebirth (not reincarnation) and the “infinite” (nirvana). Atheism is just as much in the space of ignorance since it is based on the beliefs that no gods or deities or the supernatural do exist. Such beliefs can only come into being as a reaction to others having a belief in gods or deities or the supernatural. But denial of a non-belief cannot rationally be meaningful. If religions and their belief in gods or the supernatural did not first exist, atheism would be meaningless. Atheism merely replaces a belief in a God to a belief in a Not-God.

I take the blind worship of “science” also to be a religion in the space of ignorance. All physicists and cosmologists who believe in the Big Bang singularity, effectively believe in an incomprehensible and unexplainable Creation Event. Physicists who believe in dark matter or dark energy, as mysterious things, vested with just the right properties to bring their theories into compliance with observations of an apparently expanding universe, are effectively invoking magic. When modern physics claims that there are 57 fundamental particles but has no explanation as to why there should be just 57 (for now) or 59 or 107 fundamental particles, they take recourse to magical events at the beginning of time. Why there should be four fundamental forces in our universe (magnetism, gravitation, strong force and weak force), and not two or three or seven is also unknown and magical.

Agnosticism is just a reaction to the belief in gods. Whereas atheists deny the belief, agnostics merely state that such beliefs can neither be proved or disproved; that the existence of gods or the supernatural is unknowable. But by recognising limits to what humans can know, agnosticism inherently accepts that understanding the universe lies on a “higher” dimension than what human intelligence and cognitive abilities can cope with. That is tantamount to a belief in “magic” where “magic” covers all things that happen or exist but which we cannot explain. Where atheism denies the answers of others, agnosticism declines to address the questions.

The Big Bang singularity, God(s), Nirvana and the names of all the various deities are all merely labels for things we don’t know in the space of what we don’t know, that we don’t know. They are all labels for different kinds of magic.

I am not sure where that leaves me. I follow no religion. I believe in the scientific method as a process but find the “religion of science” too self-righteous and too glib about its own beliefs in the space of ignorance. I find atheism is mentally lazy and too negative. It is just a denial of the beliefs of others. It does not itself address the unanswerable questions. It merely tears down the unsatisfactory answers of others. Agnosticism is a cop-out. It satisfies itself by saying the questions are too hard for us to ever answer and it is not worthwhile to try.

I suppose I just believe in Magic – but that too is just a label in the space of ignorance.


 

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How much of global warming is due to data corruption?

August 27, 2014

The Australian Bureau of Meteorology (BOM) is scrabbling trying to defend why the intentional corruption of data is justified. Dr. Jennifer Marohasy has a new post demonstrating that the excuses being offered do not hold up.

Whereas the Australian establishment uses “homogenisation” as their euphemism for “intentional data corruption”, the US uses “adjustment” : How NOAA Data Tampering Destroys Science

The temperature record at Rutherglen has been corrupted by managers at the Australian Bureau of Meteorology.

Of course raw data often needs to be adjusted but when the magnitude of the data adjustment is greater than the magnitude of the conclusion, then the adjustments or homogenisation become “data corruption” or ” data tampering”. As my Professor, Doug Elliott,  once told me – some 40 years ago – when I wanted to make calculated corrections for presumed errors due to radiation in flame temperature measurements, “You can argue for whatever corrections you want to make, but you cannot replace the measurement. The measurement is the measurement is the measurement”.

A “science” built on the falsification of data?

As was recently pointed out, fudging both data and model results seems endemic in “climate science”:

a recent paper from ETH Zurich.

If the model data is corrected downwards, as suggested by the ETH researchers, and

the measurement data is corrected upwards, as suggested by the British and Canadian researchers,

then the model and actual observations are very similar.

 

How climate science models are validated

August 20, 2014

Mathematical and computer models are wonderful tools. Once they can be validated they are powerful methods of interpolation. They are useful methods of improving the understanding embodied in the models by extrapolation. The divergence between extrapolated model results and real data can then be used for improving the models to better account for real data. If model results do not fit real data it is time to change the model.

Extrapolated model results are never evidence. They are just indicators of what may come to pass provided that the model – in spite of all its simplifications – does truly apply.

And when climate model results are fudged to move towards climate data which, in turn, has to be fudged to move towards the model results, one wonders whether there is any scientific method left in “climate science”.

This is reported by WUWT from a recent paper from ETH Zurich.

If the model data is corrected downwards, as suggested by the ETH researchers, and

the measurement data is corrected upwards, as suggested by the British and Canadian researchers,

then the model and actual observations are very similar.

Shameless is one adjective that comes to mind.

Fraudulent is another.

 

 

“Consensus science” – by definition – is not “science” and is a dangerous thing

April 30, 2013

The internet is full of polls that I generally find irritating. How many believe that “A” will happen? or that “B will win? or that “C” is better than “D”? Whatever the result of the poll may be, they show nothing more than where the preponderance of belief  lies. The polls are evidence only of what people believe; they are not evidence of the subject being voted upon.

Either something is or it is not.

If we don’t know whether it is or is not, we can formulate it as a hypothesis and address it by the scientific method. The formulation is then as a falsifiable hypothesis and we then predict what data might be collectable if the hypothesis was false. We then collect data and where data is not available we design and carry out experiments to provide such data. These data and their analysis should be tested – for the classical scientific method – to see if the hypothesis is false (not – it should be noted – to show that the hypothesis is true). Where the data cannot show the hypothesis to be false it means only that the hypothesis is still unproven but the data set adds to the body of evidence in favour of the hypothesis in the particular circumstances in which that data-set was collected.

When we don’t know we can still suppose the hypothesis to be true or false. But that is just a supposition and lies in the realms of belief and religion. We can take a vote within some group and see how many believe it to be true or to be false. Commercial and other interests may be vested in the supposition. Lobbying and persuasion can be applied in favour of or against the supposition. Voters can be influenced and cajoled and persuaded to vote for or against. A completely democratic and transparent system of voting may be applied. And  the result may be overwhelmingly in favour or against the supposition. But even where a majority – even an overwhelming majority of say 97% – of some group believes the proposed hypothesis to be true, the vote adds not one iota of evidence in favour of or against the hypothesis. An overwhelming vote that a hypothesis is true when it is actually false makes it no less false. All the vote can show is the preponderance of belief (and belief – by definition – comes into play when and because evidence is lacking).

And all that democratic process to establish what people believe brings us no closer to answering the question of whether the supposition is true.

But it gets worse.

Once a “democratic” majority has confirmed its belief in a supposed “truth” of a supposition, then there is a immense societal pressure against proving the supposition to be false. Falsifiable hypotheses are reformulated to be no longer falsifiable. The scientific method is perverted – for reasons of the vested interests – to now produce anecdotal evidence trying to “prove the hypothesis” rather than trying to collect data to try and show the hypothesis to be false. Evidence against the majority belief is not collected because it is no longer expedient to do so. Not only is it not collected, it is ignored even when it is plain and obvious. The moment a scientific hypothesis invokes or has to invoke a majority vote or a consensus in its support it leaves the scientific arena and enters the  political universe. Truth becomes whatever the majority believes. Proper scientific effort directed to falsifying the supposition is not just discouraged, it is penalised and attracts sanctions in the form of reduced funding and rejection of publications. It becomes heresy. Even where the believed supposition is actually true, the supposition remains as belief and cannot easily be brought back into the rational world.

As Judith Curry wrote recently:

With genuinely well-established scientific theories, ‘consensus’ is not discussed and the concept of consensus is arguably irrelevant.  For example, there is no point to discussing a consensus that the Earth orbits the sun, or that the hydrogen molecule has less mass than the nitrogen molecule.  While a consensus may arise surrounding a specific scientific hypothesis or theory, the existence of a consensus is not itself the evidence. ……. 

Given the complexity of the climate problem, ‘expert judgments’ about uncertainty and confidence levels are made by the IPCC on issues that are dominated by unquantifiable uncertainties. It is difficult to avoid concluding that the IPCC consensus is manufactured and that the existence of this consensus does not lend intellectual substance to their conclusions.

“Consensus science” has no option but to become science by majority vote. Polls replace evidence. And where the belief is false, the belief itself prevents a return to the truth. “Consensus science” as belief cannot be “science”. The simple fact is that whenever a “scientific hypothesis” invokes a consensus in its support it is – per force – just a belief. It becomes religion and not science. And that is a dangerous thing.

Related: Climate change: no consensus on consensus

We learn about climate only when the models are wrong!

March 29, 2013

When a forecast based on a mathematical model is correct, we learn nothing.

A mathematical model is merely a theory, a simplification of reality or an approximation to the real world. By definition a mathematical model is a hypothesis.  When forecasts are incorrect, we can return to our model and improve it and make a new hypothesis. A forecast is then a test of the model but in just one particular set of circumstances. Being correct does not prove the theory behind the model. It does of course add to the body of evidence that the model may be a satisfactory representation of reality and it does allow further forecasts to be made without tweaking the model. For learning to take place the mathematical model must be the falsifiable hypothesis of the scientific method.

It seems to me that Solar Science has a much healthier (scientifically) attitude to models and forecasts than “Climate Science”. When observations don’t match a climate forecast, the observations are impugned rather than the models being improved. This is, I think, because the forecast climate results have been used to establish huge revenue flows in the political arena (whether as taxes or carbon credits or just as research funding). There has been a vested interest in denying the observations and calling the science “settled”. Once the science is “settled”  the climate forecast and its underlying model become sacrosanct and take on the certainty of prophecy. Instead of being falsifiable hypotheses, climate model forecasts have taken on the character of unfalsifiable prophecies!

No scientist would presume to claim that we know or understand all solar effects. Or that we know and understand the role of the oceans or of the water vapour and dust and aerosols in the atmosphere. “Climate” is contained in the thin, chaotic layer of atmosphere which surrounds us. Yet “Climate Science” makes the arrogant assumption that the effect of trace amounts of carbon dioxide on climate is known definitively. Filling a real greenhouse with higher concentrations of carbon dioxide does not make that greenhouse any warmer than one filled with normal air – but the plants do grow faster with access to the additional CO2!! But – claim the climate priesthood –  in the real atmosphere, carbon dioxide causes other forcings (clouds? aerosols? precipitation effects?) which maximise warming which means that our model is still valid. Why not just admit that we don’t know what we don’t know?

The behavioural issue of course is whether it is worth trying to control something as poorly understood as climate rather than ensuring that we have the wherewithal to adapt to whatever changes may come. Another ice age will surely come whether in 10 years or a 100 years or 2,000. It will then be our ability to harness all available energy sources around us which will determine our capacity to adapt.

Learning from forecasts when they are wrong – not just in science but also in business and project management and technology development – has long been a hobby-horse of mine and is why forecasts need to be wrong.

When there is no difference there is no learning.

  • I take prophecies to be a promise about the future  based primarily on faith and made by prophets , witchdoctors, soothsayers and politicians such as ”You will be doomed to eternal damnation if you don’t do as I say”,
  • I take “forecasts” to be an estimate of future conditions based on known data with the use of calculations, logic, judgement, some intuition and even some faith. They are extrapolations of historical conditions to anticipate – and thereby plan for -future conditions.

……. Over the last 30 years I have spent of a lot of time conducting and participating in reviews. Reviews of research projects, of construction projects, of organisations and processes, of designs, of strategies and action plans, of businesses and of companies. The common features  in all these different reviews, that I have found the most penetrating, have been the comparisons not only between forecast values  and actual values, (which may be any values indicating performance and capable of being extrapolated), but also between past forecasts and current forecasts.

Whether considering construction progress or costs or sales figures or cash flow or profit or number of patents applied for, it is the differences between forecast and actual values, or values forecast before and values forecast later which have led to learning. In all these fields we are in the area of the behaviour of complex systems; and where people and their behaviour is involved any system is inevitably a complex system.

When a forecast is fulfilled there is usually an air of congratulation, satisfaction and self-adulation and this leads to a deadly complacency that everything is “settled science” and well understood. In any enterprise of any kind, that kind of complacency is the kiss of death. It is the differences which lead to questioning, to proper scientific scepticism, to further investigation and ultimately to an increase of understanding and – perhaps – a better forecast. (Of course, ignoring all such differences  and to merely “continue as before” can be equally fatal).

Which brings me to climate (which is not a science by any stretch of the imagination) and solar cycles. They are both in the realm not only of where “what we know is a great deal less than what we don’t know” but they are also both in the region where “we don’t even know what we don’t know”. We do not even know all the questions to be asked. They are both complex systems where – by definition – the complexity lies in the multitude of the processes involved and their interactions.

When climate – which is contained in the 100 m of ocean and 20 or so km thick, turbulent and chaotic atmospheric layer (and which is dimensionally miniscule in relation to the 140 million km of the earth-to-sun system) – is so complacently considered to be “settled science” then we have shifted into the area of faith and soothsaying and prophecies. When climate modellers are smug enough to believe they have understood the climate system and believe that their models are complete, then the models produce outputs which are not forecasts but prophecies. (No doubt soothsayers and shamans have sometimes made accurate prophecies but I still would not buy a used car from one of them)! Weather is in the realm of forecast (though you could argue that the most accurate forecast is still that “the weather tomorrow will be like today”) but climate is not yet there.

This kind of “arrogance” which pervades some of the climate “scientists” is not so prevalent when it comes to the study of Solar Cycles. There is a clear understanding that “we don’t know what we don’t know”. In addition to the 11 year and 22 year cycles, other cycles are hypothesised for 87 years, 210 years, 2300 years (or maybe 2241 or 2500 years) and 6000 years. We have no idea what causes these cycles. Even the 11 year cycle which has been most studied produces  surprises every day but is properly in the area of “forecast” (and hopefully never again will be in the area of prophecy). ….

…… We seem to be in a solar minimum. We may be seeing a 210 year cycle – or maybe not. There are changes to the forecasts not only regarding the maximum level of sunspot activity but also about when it will occur and what the length of cycle 24 might be. There is speculation as to what effect the length of the solar cycle may have on climate – but we haven’t a clue as to what mechanisms may be involved.  This is not to say that there isn’t much speculation and hypothesising. There is a great deal of comment about the effect these changing forecasts may have on global warming or cooling or climate disruption.  In some quarters there is much glee that the forecasts have been “wrong”. Some comments question the intelligence of the forecasters.

But of course the forecasts themselves say nothing about how the behaviour of the sun may impact our climate. They do not pretend to be prophecies or to be statements of inevitable outcomes. All they do say is that we don’t know very much – yet – about the sun. But we do know enough to make some tentative forecasts.

But I am very glad that people continue to be brave enough to make forecasts and I am quite relieved that the forecasts are not spot on. That at least ensures we will continue learning.

Idle thoughts: Disciplines, sciences and pseudosciences

November 4, 2012

There is virtually nothing in the physical universe around us that is not worthy of study. Most study begins with observations. We can term any such area of study where observations are made and knowledge accumulated as being a “discipline”. The social “sciences”, environmentalism and even astrology and palmistry could be considered disciplines.

But when does a discipline become a science?

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Further twists in the Italian manslaughter trial of seismologists

February 21, 2012
L'Aquila house ruin

A panel of seismologists who met just days before the 2009 earthquake in L’Aquila, Italy are on trial over their reassurances to the public. WOLFANGO VIA FLICKR UNDER CREATIVE COMMONS.

Back in September when this trial for manslaughter began, many rushed to the defence of the scientists being indicted as being an “attack on science”. I wrote then that indictments for incompetence or negligence or even gross negligence by scientists  should not be confused with being an indictment of the scientific method. Scientists are in a privileged position but that does not mean that they cannot be liable for their incompetence. As the trial lumbers on it becomes clearer that there was indeed some considerable incompetence involved. Now Nature reports that a Californian scientist and earthquake expert is testifying against the defendents:

….. The hearing also included some true scientific debate when Lalliana Mualchin, former chief seismologist for the Department of Transportation in California, testified as an expert witness for the prosecution. In 2010, when news about the indictment broke, Mualchin was among the few experts who openly criticized — and refused to sign — a letter supporting the indicted seismologists signed by about 5,000 international scientists.

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Another Nature paper retracted by authors but lead author does not sign retraction

November 9, 2010

Retraction Watch reports on the retraction of a paper at Nature by the authors but where, once again, the lead author does not sign the retraction.

In this case the paper is:

The large-conductance Ca2+-activated K+channel is essential for innate immunity by Jatinder Ahluwalia, Andrew Tinker, Lucie H. Clapp, Michael R. Duchen, Andrey Y. Abramov, Simon Pope, Muriel Nobles & Anthony W. Segal, Nature 427, 853-858 (26 February 2004), doi:10.1038/nature02356; Received 18 July 2003; Accepted 20 January 2004.

The Retraction Notice reads

The authors wish to retract this Letter after the report of an inability to reproduce their results, later confirmed by another. The studies the authors then conducted led to an internal investigation by University College London, please see the accompanying Supplementary Information for details. The retraction has not been signed by Jatinder Ahluwalia.

The lead author is usually the researcher and the last name is usually that of the senior author. There have been a number of such cases recently where the authors retract a paper but where the lead author does not sign the retraction. The inference is that there has been some misconduct or alleged misconduct by the researcher which has been “discovered” by the other authors but where the alleged misconduct is not acknowledged by the lead author. (See the cases of Shane R Mayack and Hung-Shu Chang for example). Just the fact that some data can not be reproduced does not mean that misconduct has occurred. Experimental data can never be perfect. In addition to measurement errors and procedural errors, data may also be subject to errors of interpretation and analysis. In fact the scientific method requires the publication of such data – warts and all – which can then be tested by others and retraction would not be necessary or correct merely if different results were obtained later. Erroneous data does not have to be – and should not be – deleted from the record. A retraction – and especially by a multiplicity of contributing authors but not the lead author  – carries a strong inference of misconduct.

This raises once again the question of roles and responsibilities between the different contributing authors, the reviewers and the journal editor for a published paper. Perhaps the number of retractions is at an “acceptable” level, but I am sure that the number of retractions must follow the “Iceberg Principle” and what is finally made visible can only be the tip of what must be there. The senior author must bear some responsibility and have some accountability for such events.

It seems to me that senior authors (as supervisors of the research reported) get away too lightly and merely pass the responsibility onto the researcher’s failings or his misconduct. They abdicate their responsibility for quality and integrity rather too easily. I would like to see a statement by the senior author whenever such a retraction is made “at the request of the authors”.


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