The hypothetical-deductive method is an approach to research that starts from a theory about the functioning of things and derives from it testable hypotheses. It is a form of deductive reasoning, since it starts from principles, assumptions and general ideas to arrive at more concrete statements about the appearance and functioning of the world. The hypotheses are then tested by data collection and analysis and the results support or refute the theory.

An early version of the hypothetical-deductive method was proposed by the Dutch physicist Christiaan Huygens (1629-95). In general, the method starts from the basis that well-formed theories are conjectures that aim to explain a set of observable data. However, these hypotheses cannot be conclusively established until the consequences that logically derive from them are verified by additional observations and experiments.

The method treats theory as a deductive system in which particular empirical phenomena are explained by relating them to general principles and definitions. However, it rejects the claim of Cartesian mechanics that these principles and definitions are self-evident and valid. It assumes that its validity is determined solely by the exact light that its consequences shed on previously unexplained phenomena or on real scientific problems.

Characteristics of the Hypothetical - Deductive Method

The hypothetical-deductive method is one of the pillars of scientific research, often regarded as the only "true" method of scientific research.

This area fuels intense debate and discussion among many fields of scientific specialization.

Concisely, the method involves the traditional steps of observation of the subject, to elaborate an area of study. This allows the researcher to generate a testable and realistic hypothesis.

The hypothesis must be falsifiable by recognized scientific methods, but it can never be fully confirmed, as refined research methods can subsequently refute it.

From the hypothesis, the researcher must generate some initial predictions, which can be tested, or disproved, by the experimental process. These predictions must be inherently testable for the hypothetical-deductive method to be a valid process. For example, trying to test the hypothesis that God exists would be difficult, because there is no scientific way to evaluate it.

Example of Hypotetic - Deductive Method

Suppose your music player won't turn on. You can consider the hypothesis that the batteries are exhausted. Then you decide to check if this is true.

Given this hypothesis, you predict that the music player will work properly if you replace the batteries with new ones.

You replace the batteries, which would be the experiment you use to check your prediction.

If the player works, you confirm your hypothesis (and you can throw away the old batteries). If the player still doesn't work, your prediction is false and your hypothesis is disconfirmed. You may reject your original hypothesis and come up with an alternative to test it.

Theories and tests

There are many branches of science, such as chemistry, biology, physics, etc. But, in general, there are four main components to scientific research:

Theories: are the hypotheses, laws and facts about the empirical world.

The world: all the objects, processes and properties of the universe.

Explanations and predictions: we use theories to explain what is happening 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 Earth's history can predict that certain rocks contain a high percentage of iron. A crucial part of scientific research is testing a theory by checking whether our predictions are correct or not.

Data (tests): the information we collect during observations or experiments. We use data to test our theories and inspire new directions in research.

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

What laws, principles, and facts does the theory include? What do these theories tell us about the nature of the world? Also, what can it predict and what can it explain? And finally, what are the main tests used to support the theory and is there evidence against it?

A scientific hypothesis must be testable

The Hypothetical - Deductive method tells us how to test a hypothesis and a scientific hypothesis should be susceptible to being tested.

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

Confirmation is not the truth

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

To see why this is so, we could represent our reasoning as follows:

If H then P.

P.

Therefore, H.

Here H is our hypothesis "the batteries are exhausted" and P is the prediction "the player will work when the batteries are changed". Of course, this pattern of reasoning is not valid, as there may be reasons other than H that also give rise to the truth of P. For example, it may be that the original batteries are really fine, but have not been placed correctly. If the batteries are replaced, the loose connection will be restored.

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 most likely to be true and which provides the best explanation of the prediction. Or we can also do more tests.

Disconfirmation does not have to be a falsehood

Often, a hypothesis generates a prediction only when additional assumptions (auxiliary hypotheses) are given. In these cases, when a prediction fails, the theory can remain correct.

Going back to our example, when we predict that the player will work again when the batteries are changed, we are assuming that there is nothing wrong with the player. But it may turn out that this assumption is wrong. In such situations, the falsity of the prediction does not logically imply the falsity of the hypothesis. We could represent the situation with this argument: ( H = The batteries are exhausted, 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.

Validation

This argument here is not valid, of course. 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.

Going back to our previous example, if the player still does not work when the batteries are changed, this does not conclusively prove that the original batteries are not exhausted. This tells us that when we apply the HD method, we have to examine the additional assumptions that are invoked when deriving predictions.

If we are sure that the assumptions are correct, 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 extraordinarily successful, then we must be extremely cautious before rejecting a theory on the basis of a single false prediction. These additional assumptions that are used to test a theory are known as "auxiliary hypotheses."

When should we reject a theory?

When a theory makes a false prediction, it can sometimes be difficult to know if we should reject the theory or if there is something wrong with the auxiliary hypotheses. For example, nineteenth-century astronomers discovered that Newtonian physics could not fully explain the orbit of the planet Mercury.

It turns out that this is because Newtonian physics is wrong, and relativity is needed to give a more accurate prediction of orbit. However, when astronomers discovered Uranus in 1781, they also discovered 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 affecting Uranus and subsequently Neptune was discovered as a result.

In 2011, scientists in Italy reported that their experiment appeared to have shown that some subatomic particles could travel faster than the speed of light, which would seem to prove the theory of relativity wrong. But upon closer examination, it was discovered that there was a problem with the experimental assembly. Therefore, if a theory has been successful, even when a result seems to prove the theory wrong, we must ensure that the evidence is solid and reliable and try to replicate the result and eliminate alternative explanations. An extraordinary claim requires extraordinary proof.

Generate and analyze the data

The next stage is to perform the experiment, obtaining statistically verifiable results, which can be used to analyze the results and determine if the hypothesis has validity or has little foundation. This experiment must involve some manipulation of the variables to allow the generation of analyzable data.

Finally, statistical tests will confirm whether the predictions are correct or not. This method is usually so rigorous that it is rare for a hypothesis to be fully proven, but some of the initial predictions may be correct and will lead to new areas of research and refining the hypothesis.

Evaluation of the validity of the hypothesis

Testing and confirming a hypothesis is never a clear and definitive process. Statistics is a science based on probability and, no matter how solid the results generated, there is always the possibility of an experimental error.

In addition, there may be another unknown reason that explains the results. Most theories, however solid the proof, develop and evolve over time, changing and adapting as new research refines the known data.

The testing of a hypothesis is never entirely accurate, but, after a process of debate and re-checking of the results, it can become a scientific assumption. Science is based on these paradigms and even commonly accepted opinions can prove inaccurate upon further exploration.

A false hypothesis does not necessarily mean that the research area is closed or incorrect. The experiment may not have been accurate enough or there may have been some other error contributing to it.

That is why the hypothetical-deductive method is based on initial predictions: very few hypotheses. If the research is thorough, they are completely wrong, as they generate new directions for future research.

Two misconceptions

Here are two common misconceptions about science:

Some people like to say that science cannot prove anything, that everything is based on faith, like religion. The correct thing about this view is that scientists cannot prove most scientific claims because they are not 100% sure. But this does not mean that accepting science is solely a matter of faith: we can have solid evidence to support scientific theories. We have to be content with probability, not absolute certainty. For example, we cannot prove with 100% certainty that one will die if one jumps out of a plane without a parachute. In fact, some people have survived. However, it would be foolish to try just because we lack this proof.

In science, we call "theory" a set of statements and principles on a particular topic. 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 statements are neither helpful nor clear. It's true that scientists consider them theories, but that doesn't mean they're speculative hypotheses that live up to any wild guess people make up. A scientific theory can describe a claim that a wide range of evidence strongly supports. To say it's "just" a theory is unfair.

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Sources Consulted

Hammond, K. undated, 'What is the difference between the scientific method & the hypothetico-deductive model?' on |eHow.com (1999-2013). In: http://www.ehow.com/info_10055668_difference-between-scientific-method-hypotheticodeductive-model.html.

iSTAR Assessment, 2010-2011, 'Hypothetical-Deductive Reasoning', at http://www.istarassessment.org/srdims/hypothetical-deductive-reasoning-needs-pictures/

Jary, D., 2006, 'Hypothetico-deductive model', in Jupp, V (Ed.) 'The Sage Dictionary of Social Research Methods , available at http://srmo.sagepub.com/view/the-sage-dictionary-of-social-research-methods/n94.xml, accessed 6 March 2013, page not freely available 22 December 2016.

Hypothetical Deductive Method

Hypothetical Deductive Method. Photo: Unsplash. Credits: Ale Cisneros @alecisnros

 

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