Tag Archives: falsifiability

Climate change has changed the way I think about science. Here’s why

The Conversation

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Science is a human approach to understanding the world. Nitirak Rakitiworakun/shutterstock

Sophie Lewis, Australian National University

I’ve wanted to be a scientist since I was five years old.

My idea of a scientist was someone in a lab, making hypotheses and testing theories. We often think of science only as a linear, objective process. This is also the way that science is presented in peer reviewed journal articles – a study begins with a research question or hypothesis, followed by methods, results and conclusions.

It turns out that my work now as a climate scientist doesn’t quite gel with the way we typically talk about science and how science works.

Climate change, and doing climate change research, has changed the way I see and do science. Here are five points that explain why.


Read more: Australia needs dozens more scientists to monitor climate properly


1. Methods aren’t always necessarily falsifiable

Falsifiability is the idea that an assertion can be shown to be false by an experiment or an observation, and is critical to distinctions between “true science” and “pseudoscience”.

Climate models are important and complex tools for understanding the climate system. Are climate models falsifiable? Are they science? A test of falsifiability requires a model test or climate observation that shows global warming caused by increased human-produced greenhouse gases is untrue. It is difficult to propose a test of climate models in advance that is falsifiable.

Science is complicated – and doesn’t always fit the simplified version we learn as children. FoxyImage/shutterstock

This difficulty doesn’t mean that climate models or climate science are invalid or untrustworthy. Climate models are carefully developed and evaluated based on their ability to accurately reproduce observed climate trends and processes. This is why climatologists have confidence in them as scientific tools, not because of ideas around falsifiability.

2. There’s lots of ways to interpret data

Climate research is messy. I spent four years of my PhD reconstructing past changes in Australian and Indonesian rainfall over many thousands of years. Reconstructing the past is inherently problematic. It is riddled with uncertainty and subject to our individual interpretations.

During my PhD, I submitted a paper for publication detailing an interpretation of changes in Indonesian climates, derived from a stalagmite that formed deep in a cave.

My coauthors had disparate views about what, in particular, this stalagmite was telling us. Then, when my paper was returned from the process of peer review, seemingly in shreds, it turns out the two reviewers themselves had directly opposing views about the record.

What happens when everyone who looks at data has a different idea about what it means? (The published paper reflects a range of different viewpoints).

Another example of ambiguity emerged around the discussion of the hiatus in global warming. This was the temporary slowdown in the rate of global warming at the Earth’s surface occurring roughly over the 15 year period since 1997. Some sceptics were adamant that this was unequivocal proof that the world was not warming at all and that global warming was unfounded.

There was an avalanche of academic interest in the warming slowdown. It was attributed to a multitude of causes, including deep ocean processes, aerosols, measurement error and the end of ozone depletion.

Ambiguity and uncertainty are key parts of the natural world, and scientific exploration of it.

3. Sometimes the scientist matters as well as the results

I regularly present my scientific results at public lectures or community events. I used to show a photo depicting a Tasmanian family sheltering under a pier from a fire front. The sky is suffused with heat. In the ocean, a grandmother holds two children while their sister helps her brother cling to underside of the pier.

After a few talks, I had to remove the photo from my PowerPoint presentation because each time I turned around to discuss it, it would make me teary. I felt so strongly that the year we were living was a chilling taste of our world to come.

Just outside of Sydney, tinderbox conditions occurred in early spring of 2013, following a dry, warm winter. Bushfires raged far too early in the season. I was frightened of a world 1°C hotter than now (regardless of what the equilibrium climate sensitivity turns out to be).

At public lectures and community events, people want to know that I am frightened about bushfires. They want to know that I am concerned about the vulnerability of our elderly to increasing summer heat stress. People want to know that, among everything else, I remain optimistic about our collective resilience and desire to care for each other.


Read more: Distrust of experts happens when we forget they are human beings


Communicating how we connect with scientific results is also important part of the role of climate scientists. That photo of the family who survived the Tasmanian bushfire is now back in my presentations.

4. Society matters too

In November 2009, computer servers at the University of East Anglia were illegally hacked and email correspondence was stolen.

A selection of these emails was published publicly, focusing on quotes that purported to reveal dishonest practices that promoted the myth of global warming. The “climategate” scientists were exhaustively cleared of wrongdoing.

On the surface, the climategate emails were an unpleasant but unremarkable event. But delving a little deeper, this can be seen as a significant turning point in society’s expectations of science.

While numerous fastidious reviews of the scientists cleared them of wrongdoing, the strong and ongoing public interest in this matter demonstrates that society wants to know how science works, and who “does” science.

There is a great desire for public connection with the processes of science and the outcomes of scientific pursuits. The public is not necessarily satisfied by scientists working in universities and publishing their finding in articles obscured by pay walls, which cannot be publicly accessed.

A greater transparency of science is required. This is already taking off, with scientists communicating broadly through social and mainstream media and publishing in open access journals.

5. Non-experts can be scientists

Climate science increasingly recognises the value of citizen scientists.

Enlisting non-expert volunteers allows researchers to investigate otherwise very difficult problems, for example when the research would have been financially and logistically impossible without citizen participation.


Read more: Exoplanet discovery by an amateur astronomer shows the power of citizen science


The OzDocs project involved volunteers digitising early records of Australian weather from weather journals, government gazettes, newspapers and our earliest observatories. This project provided a better understanding of the climate history of southeastern Australia.

Personal computers also provide another great tool for citizen collaborators. In one ongoing project, climate scientists conduct experiments using publicly volunteered distributed computing. Participants agree to run experiments on their home or work computers and the results are fed back to the main server for analysis.

While we often think of scientists as trained experts working in labs and publishing in scholarly journals, the lines aren’t always so clear. Everyone has an opportunity to contribute to science.

My new book explores this space between the way science is discussed and the way it takes place.

The ConversationThis isn’t a criticism of science, which provides a useful way to explore and understand the natural world. It is a celebration of the richness, diversity and creativity of science that drives this exploration.

Sophie Lewis, Research fellow, Australian National University

This article was originally published on The Conversation. (Reblogged by permission). Read the original article.

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What is logic?

The word ‘logic‘ is not easy to define, because it has slightly different meanings in various applications ranging from philosophy, to mathematics to computer science. In philosophy, logic’s main concern is with the validity or cogency of arguments. The essential difference between informal logic and formal logic is that informal logic uses natural language, whereas formal logic (also known as symbolic logic) is more complex and uses mathematical symbols to overcome the frequent ambiguity or imprecision of natural language.

So what is an argument? In everyday life, we use the word ‘argument’ to mean a verbal dispute or disagreement (which is actually a clash between two or more arguments put forward by different people). This is not the way this word is usually used in philosophical logic, where arguments are those statements a person makes in the attempt to convince someone of something, or present reasons for accepting a given conclusion. In this sense, an argument consist of statements or propositions, called its premises, from which a conclusion is claimed to follow (in the case of a deductive argument) or be inferred (in the case of an inductive argument). Deductive conclusions usually begin with a word like ‘therefore’, ‘thus’, ‘so’ or ‘it follows that’.

A good argument is one that has two virtues: good form and all true premises. Arguments can be either deductiveinductive  or abductive. A deductive argument with valid form and true premises is said to be sound. An inductive argument based on strong evidence is said to be cogent. The term ‘good argument’ covers all three of these types of arguments.

Deductive arguments

A valid argument is a deductive argument where the conclusion necessarily follows from the premises, because of the logical structure of the argument. That is, if the premises are true, then the conclusion must also be true. Conversely, an invalid argument is one where the conclusion does not logically follow from the premises. However, the validity or invalidity of arguments must be clearly distinguished from the truth or falsity of its premises. It is possible for the conclusion of a valid argument to be true, even though one or more of its premises are false. For example, consider the following argument:

Premise 1: Napoleon was German
Premise 2: All Germans are Europeans
Conclusion: Therefore, Napoleon was European

The conclusion that Napoleon was European is true, even though Premise 1 is false. This argument is valid because of its logical structure, not because its premises and conclusion are all true (which they are not). Even if the premises and conclusion were all true, it wouldn’t necessarily mean that the argument was valid. If an argument has true premises and its form is valid, then its conclusion must be true.

Deductive logic is essentially about consistency. The rules of logic are not arbitrary, like the rules for a game of chess. They exist to avoid internal contradictions within an argument. For example, if we have an argument with the following premises:

Premise 1: Napoleon was either German or French
Premise 2: Napoleon was not German

The conclusion cannot logically be “Therefore, Napoleon was German” because that would directly contradict Premise 2. So the logical conclusion can only be: “Therefore, Napoleon was French”, not because we know that it happens to be true, but because it is the only possible conclusion if both the premises are true. This is admittedly a simple and self-evident example, but similar reasoning applies to more complex arguments where the rules of logic are not so self-evident. In summary, the rules of logic exist because breaking the rules would entail internal contradictions within the argument.

Inductive arguments

An inductive argument is one where the premises seek to supply strong evidence for (not absolute proof of) the truth of the conclusion. While the conclusion of a sound deductive argument is supposed to be certain, the conclusion of a cogent inductive argument is supposed to be probable, based upon the evidence given. An example of an inductive argument is: 

Premise 1: Almost all people are taller than 26 inches
Premise 2: George is a person
Conclusion: Therefore, George is almost certainly taller than 26 inches

Whilst an inductive argument based on strong evidence can be cogent, there is some dispute amongst philosophers as to the reliability of induction as a scientific method. For example, by the problem of induction, no number of confirming observations can verify a universal generalization, such as ‘All swans are white’, yet it is logically possible to falsify it by observing a single black swan.

Abductive arguments

Abduction may be described as an “inference to the best explanation”, and whilst not as reliable as deduction or induction, it can still be a useful form of reasoning. For example, a typical abductive reasoning process used by doctors in diagnosis might be: “this set of symptoms could be caused by illnesses X, Y or Z. If I ask some more questions or conduct some tests I can rule out X and Y, so it must be Z.

Incidentally, the doctor is the one who is doing the abduction here, not the patient. By accepting the doctor’s diagnosis, the patient is using inductive reasoning that the doctor has a sufficiently high probability of being right that it is rational to accept the diagnosis. This is actually an acceptable form of the Argument from Authority (only the deductive form is fallacious).

References:

Hodges, W. (1977) Logic – an introduction to elementary logic (2nd ed. 2001) Penguin, London.
Lemmon, E.J. (1987) Beginning Logic. Hackett Publishing Company, Indianapolis.

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