Tag Archives: scientific method

What exactly is the scientific method and why do so many people get it wrong?

The Conversation

Peter Ellerton, The University of Queensland

Claims that the “the science isn’t settled” with regard to climate change are symptomatic of a large body of ignorance about how science works.

So what is the scientific method, and why do so many people, sometimes including those trained in science, get it so wrong?

The first thing to understand is that there is no one method in science, no one way of doing things. This is intimately connected with how we reason in general.

Science and reasoning

Humans have two primary modes of reasoning: deduction and induction. When we reason deductively, we tease out the implications of information already available to us.

For example, if I tell you that Will is between the ages of Cate and Abby, and that Abby is older than Cate, you can deduce that Will must be older than Cate.

That answer was embedded in the problem, you just had to untangle it from what you already knew. This is how Sudoku puzzles work. Deduction is also the reasoning we use in mathematics.

Inductive reasoning goes beyond the information contained in what we already know and can extend our knowledge into new areas. We induce using generalisations and analogies.

Generalisations include observing regularities in nature and imagining they are everywhere uniform – this is, in part, how we create the so-called laws of nature.

Generalisations also create classes of things, such as “mammals” or “electrons”. We also generalise to define aspects of human behaviour, including psychological tendencies and economic trends.

Analogies make claims of similarities between two things, and extend this to make new knowledge.

For example, if I find a fossilised skull of an extinct animal that has sharp teeth, I might wonder what it ate. I look for animals alive today that have sharp teeth and notice they are carnivores.

Reasoning by analogy, I conclude that the animal was also a carnivore.

Using induction and inferring to the best possible explanation consistent with the evidence, science teaches us more about the world than we could simply deduce.

Saber tooth cat skull: just look at the fangs. Flickr/Badlands National Park

Science and uncertainty

Most of our theories or models are inductive analogies with the world, or parts of it.

If inputs to my particular theory produce outputs that match those of the real world, I consider it a good analogy, and therefore a good theory. If it doesn’t match, then I must reject it, or refine or redesign the theory to make it more analogous.

If I get many results of the same kind over time and space, I might generalise to a conclusion. But no amount of success can prove me right. Each confirming instance only increases my confidence in my idea. As Albert Einstein famously said:

No amount of experimentation can ever prove me right; a single experiment can prove me wrong.

Einstein’s general and special theories of relativity (which are models and therefore analogies of how he thought the universe works) have been supported by experimental evidence many times under many conditions.

We have great confidence in the theories as good descriptions of reality. But they cannot be proved correct, because proof is a creature that belongs to deduction.

The hypothetico-deductive method

Science also works deductively through the hypothetico-deductive method.

It goes like this. I have a hypothesis or model that predicts that X will occur under certain experimental conditions. Experimentally, X does not occur under those conditions. I can deduce, therefore, that the theory is flawed (assuming, of course, we trust the experimental conditions that produced not-X).

Under these conditions, I have proved that my hypothesis or model is incorrect (or at least incomplete). I reasoned deductively to do so.

But if X does occur, that does not mean I am correct, it just means that the experiment did not show my idea to be false. I now have increased confidence that I am correct, but I can’t be sure.

If one day experimental evidence that was beyond doubt was to go against Einstein’s predictions, we could deductively prove, through the hypothetico-deductive method, that his theories are incorrect or incomplete. But no number of confirming instances can prove he is right.

That an idea can be tested by experiment, that there can be experimental outcomes (in principle) that show the idea is incorrect, is what makes it a scientific one, at least according to the philosopher of science Karl Popper.

As an example of an untestable, and hence unscientific position, take that held by Australian climate denialist and One Nation Senator Malcolm Roberts. Roberts maintains there is no empirical evidence of human-induced climate change.

When presented with authoritative evidence during an episode of the ABC’S Q&A television debating show recently, he claimed that the evidence was corrupted.

Professor Brian Cox explains climate science to senator Malcolm Roberts.

Yet his claim that human-induced climate change is not occurring cannot be put to the test as he would not accept any data showing him wrong. He is therefore not acting scientifically. He is indulging in pseudoscience.

Settled does not mean proved

One of the great errors in the public understanding of science is to equate settled with proved. While Einstein’s theories are “settled”, they are not proved. But to plan for them not to work would be utter folly.

As the philosopher John Dewey pointed out in his book Logic: The Theory of Inquiry:

In scientific inquiry, the criterion of what is taken to be settled, or to be knowledge, is [of the science] being so settled that it is available as a resource in further inquiry; not being settled in such a way as not to be subject to revision in further inquiry.

Those who demand the science be “settled” before we take action are seeking deductive certainty where we are working inductively. And there are other sources of confusion.

One is that simple statements about cause and effect are rare since nature is complex. For example, a theory might predict that X will cause Y, but that Y will be mitigated by the presence of Z and not occur at all if Q is above a critical level. To reduce this to the simple statement “X causes Y” is naive.

Another is that even though some broad ideas may be settled, the details remain a source of lively debate. For example, that evolution has occurred is certainly settled by any rational account. But some details of how natural selection operates are still being fleshed out.

To confuse the details of natural selection with the fact of evolution is highly analogous to quibbles about dates and exact temperatures from modelling and researching climate change when it is very clear that the planet is warming in general.

When our theories are successful at predicting outcomes, and form a web of higher level theories that are themselves successful, we have a strong case for grounding our actions in them.

The mark of intelligence is to progress in an uncertain world and the science of climate change, of human health and of the ecology of our planet has given us orders of magnitude more confidence than we need to act with certitude.

Demanding deductive certainty before committing to action does not make us strong, it paralyses us.

The ConversationPeter Ellerton, Lecturer in Critical Thinking, The University of Queensland

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

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Is this the worst popular philosophy piece ever? A philosopher argues that science is no more reliable than philosophy at finding truth

Why Evolution Is True

When I read the title of this New York Times piece in The Stone philosophy section, “There is no scientific method,” I thought at first it would be about Paul Feyerabend’s contention that, in science, “anything goes.” I discuss this in Faith versus Fact, agreeing that the classic presentation of “The Scientific Method” in the classroom is misleading.  That presentation usually goes like this: concoct hypothesis—> test hypothesis —>support or reject hypothesis based on test.

But not all science is done like that. For example, facts usually precede hypotheses, at least in biology (Darwin often used that method to concoct his theories).  And much good science can be done without any hypotheses at all. An example would be describing all the species in an area like a patch of Amazonian rain forest. Those facts may be useful some day (e.g., for conservation or for finding new drugs from plants)…

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Feynman on scientific method

Physicist Prof. Richard Feynman explains the scientific and unscientific methods of understanding nature.


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Common sense fallacy

by Tim Harding

The American writer H L Mencken once said “There is always a well-known solution to every human problem — neat, plausible, and wrong.” He was referring to ‘common sense’, which can be superficially plausible and sometimes right, but often wrong.

The Common Sense Fallacy (or ‘Appeal to Common Sense’) is somewhat related to the Argument from Popularity and/or  the Argument from Tradition. However, it differs from these fallacies by not necessarily relying on popularity or tradition.

Instead, common sense relies on the vague notion of ‘obviousness’, which means something like ‘what we perceive from personal experience’ or ‘what we should know without having had to learn.’ In other words, common sense is not necessarily supported by evidence or reasoning. As such, beliefs based on common sense are unreliable.  The fallacy lies in giving too much weight to common sense in drawing conclusions, at the expense of evidence and reasoning.

In some ways, scientific methods have been developed to avoid the errors that can result from common sense. For instance, common sense used to tell us that the Earth is flat and that the Sun revolves around the Earth – because that is the way things appear to us without scientific investigation.  Another example of ‘common sense’ is that the world appears to have been designed, so therefore there must have been a designer.

Einstein’s theories of relativity were initially resisted, even by the scientific community, because they defied common sense.  They seemed to belong more in the realm of science fiction than reality, until they were later verified by scientific observations.  Our modern Global Positioning System (GPS) now uses Einstein’s relativity theories.  This initial resistance may have led Einstein to later say that ”Common sense is nothing more than a deposit of prejudices laid down by the mind before you reach eighteen” .

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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).


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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