The Hypothetical-Deductive Method

Read these two tutorials on scientific reasoning. Science itself is almost infinitely varied, but its basic method is surprisingly simple. These tutorials will introduce you to the four components of the hypothetical-deductive method and the difference between truth and confirmation.

Theories & Evidence

There are lots of different branches of science, such as chemistry, biology, physics, etc. But generally speaking, there are four main components in scientific research:

  1. Theories – these are the hypotheses, laws, and facts about the empirical world.
  2. The world – all the different objects, processes, and properties of the universe.
  3. Explanations and predictions – we use theories to explain what is going on in the world and make predictions. Most predictions are about the future, but we can also make predictions about the past (retrodictions). For example, a geological theory about the earth's history may predict that certain rocks contain a high percentage of iron. A crucial part of scientific research is to test a theory by checking whether our predictions are correct or not.
  4. Data (evidence) – the information we gather during observations or experiments. We use data to test our theories and inspire new directions in research.

To understand a scientific theory, we need to be able to say:

  1. What laws, principles, and facts does the theory include?
  2. What do these theories tell us about the nature of the world?
  3. What can it predict and what can it explain? and
  4. What are the main pieces of evidence used to support the theory, and is there evidence against the theory?

Exercise #1

    • List the four main components of an area of science you are familiar with.

§1. Two Misconceptions

Here are common two misconceptions about science:

    • Some people like to say that science can prove nothing – it is all based on faith, such as religion. What is right about this view is that scientists cannot prove most scientific claims because they are not 100 percent certain. But this does not mean that accepting science is solely a matter of faith – we can still have strong evidence to support scientific theories. We need to be content with probability, not absolute certainty. For example, we cannot prove with 100 percent certainty that you will die if you jump out of an airplane without a parachute. In fact, a few people have survived. However, it would be foolish to try it just because we lack this proof.

    • In science, we call a set of claims and principles about some particular subject matter a "theory." This can be misleading because we often use the word "theory" to describe a tentative hypothesis that has little evidence to support it. Some people say that "evolution is just a theory," or that  "Einstein's theories are just that – theories." These claims are not helpful or clear. It is true scientists regard them as theories, but it does not mean they are speculative hypotheses on par with any wild guesses people contrive. A scientific theory can describe a claim that a wide range of evidence strongly supports. Saying that it is "just" a theory is unfair.

§2. The Hypothetical-Deductive Method

The hypothetical-deductive method (HD method) is an important method for testing theories or hypotheses. Many call it the "scientific method." This is not quite correct because scientists can use more than one method. However, the HD method is of central importance, because it is one of the basic methods common to all scientific disciplines, whether it is economics, physics, or biochemistry. We can divide its application into four stages.

The Hypothetical-Deductive Method

  1. Identify the hypothesis to be tested.
  2. Generate predications from the hypothesis.
  3. Use experiments to check whether predictions are correct.
  4. If the predictions are correct, then the hypothesis is confirmed. If not, then the hypothesis is disconfirmed.


Here is an illustration:

  1. Suppose your music player fails to switch on. You may consider the hypothesis that the batteries are dead. So you decide to test whether this is true.
  2. Given this hypothesis, you predict the music player will work properly if you replace the batteries with new ones.
  3. You replace the batteries – the "experiment" you use to test your prediction.
  4. If the player works, you confirm your hypothesis (and you can throw out the old batteries). If the player still does not work, your prediction is false, and your hypothesis is disconfirmed. You might reject your original hypothesis and come up with an alternative one to test.

§3. Some Comments

The example above helps us illustrate a few points about science and the HD method.

1. A Scientific Hypothesis Must be Testable

The HD method tells us how to test a hypothesis, and a scientific hypothesis must be one that is capable of being tested.

If a hypothesis cannot be tested, we cannot find evidence to show that it is probable or not. In that case it cannot be part of scientific knowledge. Consider the hypothesis that there are ghosts we cannot see and can never interact with, and which can never be detected either directly or indirectly. This hypothesis is defined in such a way to exclude the possibility of testing. It might still be true and there might be such ghosts, but we would never be in a position to know and so this cannot be a scientific hypothesis.

2. Confirmation is not Truth

In general, confirming the predictions of a theory increases the probability that a theory is correct. But in itself this does not prove conclusively that the theory is correct.

To see why this is the case, we might represent our reasoning as follows:

If H then P.
Therefore H.

Here H is our hypothesis "the batteries are dead," and P is the prediction "the player will function when the batteries are replaced." This pattern of reasoning is of course not valid, since there might be reasons other than H that also bring about the truth of P. For example, it might be that the original batteries are actually fine, but they were not inserted properly. Replacing the batteries would then restore the loose connection.

The fact that the prediction is true does not prove that the hypothesis is true. We need to consider alternative hypotheses and see which is more likely to be true and which provides the best explanation of the prediction. (Or we can also do more testing!)

In the next tutorial we shall talk about the criteria that help us choose between alternative hypotheses.

3. Disconfirmation Need not be Falsity

Often a hypothesis generates a prediction only when given additional assumptions (auxiliary hypotheses). In such cases, when a prediction fails the theory might still be correct.

Looking back at our example, when we predict that the player will work again when the batteries are replaced, we are assuming that there is nothing wrong with the player. But it might turn out that this assumption is wrong. In such situations the falsity of the prediction does not logically entail the falsity of the hypothesis. We might depict the situation by this argument: ( H=The batteries are dead, A=The player is not broken.)

If ( H and A ) then P.

It is not the case that P.

Therefore, it is not the case that H.

This argument here is of course not valid. When P is false, what follows is not that H is false, only that the conjunction of H and A is false. So there are three possibilities: (a) H is false but A is true, (b) H is true but A is false, or (c) both H and A are false.

So we should argue instead:

If ( H and A ) then P.

It is not the case that P.

Therefore, it is not the case that H and A are both true.

Returning to our earlier example, if the player still does not work when the batteries are replaced, this does not prove conclusively that the original batteries are not dead. This tells us that when we apply the HD method, we need to examine the additional assumptions that are invoked when deriving the predictions. If we are confident that the assumptions are correct, then the falsity of the prediction would be a good reason to reject the hypothesis. On the other hand, if the theory we are testing has been extremely successful, then we need to be extremely cautious before we reject a theory on the basis of a single false prediction. These additional assumptions used in testing a theory are known as "auxiliary hypotheses."

§4. When Should we Reject a Theory?

When a theory makes a false prediction, sometimes it can be difficult to know whether we should reject the theory or whether there is something wrong with the auxiliary hypotheses. For example, astronomers in the 19th century found that Newtonian physics could not fully explain planet Mercury's orbit. It turns out that this is because Newtonian physics is wrong, and you need relativity to give a more accurate prediction of the orbit. However, when astronomers discovered Uranus in 1781, they also found out that its orbit was different from the predictions of Newtonian physics. But then scientists realized that it could be explained if there was an additional planet which affected Uranus, and Neptune was subsequently discovered as a result.

In 2011, scientists in Italy reported that their experiment seemed to have shown that some subatomic particles could travel faster than the speed of light, which would seem to show that relativity theory is wrong. But on closer inspection, it was discovered that there was a problem with the experimental setup. So if a theory has been successful, even when a result seems to show that theory is wrong, we need to make sure that the evidence is strong and reliable, and try to replicate the result and eliminate alternative explanations. "Extraordinary claim requires extraordinary evidence."

Source: Joe Lau and Jonathan Chan,
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Last modified: Wednesday, September 11, 2019, 4:35 PM