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Ontology refers to inquiries about reality—or Absolute Truth.
Science cannot know reality because reality cannot be observed. Thus, science can never know the Absolute Truth.
Science, must limit itself to investigations of observable phenomena to make inferences about reality.
Science makes inferences about reality within the context of theories—empirically falsifiable sets of abstract statements about reality.
Theories make statements about reality.
These statements use abstract concepts—ones that can refer to a broad range of observable phenomena.
Theories can be falsified by observation
Because science investigates only observable phenomena, it can never verify a theory, but only find reasons to infer support for it.
Thus, while one might observe a relationship that is predicted by a theory, the theory itself can never be proven as correct.
A common misconception about science is that it attempts to know Absolute Truth, that it seeks ultimate cause.
Science does not attempt to know Absolute Truth. Rather, it attempts to provide an explanation of phenomena, an explanation that can be falsified by observation.
Consider, for example, the theory of evolution. While it is possible to observe a relationship between the physical environment and animal physiology, there is no attempt to claim that evolution represents Absolute Truth because it is impossible to prove that this relationship occurs due to evolution.
Instead, one would infer support for the theory of evolution by observing relationships predicted by the theory.
The procedure used in science to infer support for a theory is the attempt to falsify null hypotheses—ones that contradict the theory.
For example, if scientists falsify the null hypothesis that there is no relationship between physical environment and animal physiology, then they assert that they have found support for the theory of evolution.
Affirming the Consequent
Consider this series of statements (shown in the format of a syllogism):
Premise: If p, then q.
Observation: q.
Conclusion: Therefore, p.
Note the logical fallacy: q might occur for other reasons than p. So, if we infer p from observing q, then we have made a logical fallacy.
Affirming the Consequent (Continued)
For example:
If it’s raining (p), then the streets are wet (q).
The streets are wet (q).
Therefore, it must be raining (p).
The streets might be wet, however, because:
It was raining, but is no longer raining.
The streets were just cleaned.
Snow is melting.
Affirming the Consequent (Continued)
To avoid affirming the consequent, statements in science are written as hypotheses.
Hypotheses have two forms:
the research hypothesis.
the null hypothesis.
The research hypothesis is the statement about reality:
There is a relationship between x and y.
The null hypothesis is the counter argument to this statement:
There is no relationship between x and y.
Two essential elements of this logic are:
One tests the hypothesis of no relationship.
Both elements of the tested hypothesis must be observable.
Consider this example:
The Angræcum sesquipedale is an orchid in Madagascar with a thinly shaped nectary of about 11-12 inches in length.
Upon observing this orchid, Charles Darwin, based upon the theory of evolution, made a startling statement: There must be a moth with a proboscis of 11-12 inches!
No such creature had ever been seen or even imagined.
So, Darwin was making a truly outlandish prediction!
(Twenty-one years after Darwin’s death, such a moth was found in Madagascar.)
To illustrate the fallacy of making deterministic statements about ultimate cause, let’s place Darwin’s prediction within the format of a syllogism (in a very abbreviated form):
Premise: If evolution, then moth with long proboscis.
Observation: moth with long proboscis.
Conclusion: evolution.
You can see the logical fallacy. We have affirmed the consequent (moth with long proboscis) to conclude that an unobservable cause (evolution) is valid.
Because there might be other explanations for a moth with a long proboscis, we need to avoid affirming the consequent when investigating reality.
The solution is to use the format of the research and null hypotheses.
What Darwin was saying, to use the format of the research hypothesis (Ha), is:
There is a relationship between physical environment and animal physiology.
or: The orchid (environment) must have a moth
that fits it and can pollinate it (physiology).
To test this research hypothesis, however, would affirm the consequent.
Therefore, scientists test the null hypothesis.
The null hypothesis (Ho) is a statement that asserts no relationship between two variables.
There is no relationship between physical environment and animal physiology.
If the null hypothesis is rejected—if there appears to be a relationship between physical environment and animal physiology—then the scientist infers support for the research hypothesis that there is a relationship between physical environment and animal physiology.
If the null hypothesis is not rejected, then the research hypothesis is falsified; it is shown to be false.
If the null hypothesis is rejected—if there appears to be a relationship between physical environment and animal physiology—then the scientist infers support for the research hypothesis that there is a relationship between physical environment and animal physiology.
If the null hypothesis is not rejected, then the research hypothesis is falsified; it is shown to be false.
Recognize that because science cannot verify an hypothesis, is it also impossible to verify the falsification of an hypothesis.
Science therefore must rely upon probabilistic statements rather than deterministic ones.
Science therefore tests hypotheses within a margin of error, which is called the Type-I error. The social sciences typically use a margin of error equal to 5%.
We have described four requirements for an epistemology to be defined as science:
Phenomena of interest must be observable,
Hypotheses must be tested in the null form,
Conclusions are stated as probabilistic—within a margin of error.
The scientist must specify conditions under which the theory being tested can be falsified.
The implications of these requirements are that science can never confirm that a theory accurately represents reality, even if no counter-examples to the theory are ever found.
Observed relationships might be the result of the assertions of the theory.
Or, they might occur for some other reason.
For example, science can never confirm that evolution is the ultimate cause of the relationship between physical environment and animal physiology, even if no counter-examples are ever found.
The relationship might be the result of evolution.
Or, it might be the result of some other cause, such as intelligent design, for example.
Intelligent design is the claim that "certain features of the universe and of living things are best explained by an intelligent cause, not an undirected process such as natural selection."
It is a modern form of the traditional teleological argument for the existence of God, modified to avoid specifying the nature or identity of the designer.
Recall, however, that a requirement of science is that theories must be capable of being falsified by observing counter-examples.
When Darwin predicted the existence of a moth with a proboscis of 11-12 inches in length, for example, he was willing to risk his theory on the outcome of finding this moth.
Now, consider this correct form of a syllogism:
Premise: If A, then B.
Observation: A.
Conclusion: Then B.
Ontology refers to inquiries about reality—or Absolute Truth.
Science cannot know reality because reality cannot be observed. Thus, science can never know the Absolute Truth.
Science, must limit itself to investigations of observable phenomena to make inferences about reality.
Science makes inferences about reality within the context of theories—empirically falsifiable sets of abstract statements about reality.
Theories make statements about reality.
These statements use abstract concepts—ones that can refer to a broad range of observable phenomena.
Theories can be falsified by observation
Because science investigates only observable phenomena, it can never verify a theory, but only find reasons to infer support for it.
Thus, while one might observe a relationship that is predicted by a theory, the theory itself can never be proven as correct.
A common misconception about science is that it attempts to know Absolute Truth, that it seeks ultimate cause.
Science does not attempt to know Absolute Truth. Rather, it attempts to provide an explanation of phenomena, an explanation that can be falsified by observation.
Consider, for example, the theory of evolution. While it is possible to observe a relationship between the physical environment and animal physiology, there is no attempt to claim that evolution represents Absolute Truth because it is impossible to prove that this relationship occurs due to evolution.
Instead, one would infer support for the theory of evolution by observing relationships predicted by the theory.
The procedure used in science to infer support for a theory is the attempt to falsify null hypotheses—ones that contradict the theory.
For example, if scientists falsify the null hypothesis that there is no relationship between physical environment and animal physiology, then they assert that they have found support for the theory of evolution.
Affirming the Consequent
Consider this series of statements (shown in the format of a syllogism):
Premise: If p, then q.
Observation: q.
Conclusion: Therefore, p.
Note the logical fallacy: q might occur for other reasons than p. So, if we infer p from observing q, then we have made a logical fallacy.
Affirming the Consequent (Continued)
For example:
If it’s raining (p), then the streets are wet (q).
The streets are wet (q).
Therefore, it must be raining (p).
The streets might be wet, however, because:
It was raining, but is no longer raining.
The streets were just cleaned.
Snow is melting.
Affirming the Consequent (Continued)
To avoid affirming the consequent, statements in science are written as hypotheses.
Hypotheses have two forms:
the research hypothesis.
the null hypothesis.
The research hypothesis is the statement about reality:
There is a relationship between x and y.
The null hypothesis is the counter argument to this statement:
There is no relationship between x and y.
Two essential elements of this logic are:
One tests the hypothesis of no relationship.
Both elements of the tested hypothesis must be observable.
Consider this example:
The Angræcum sesquipedale is an orchid in Madagascar with a thinly shaped nectary of about 11-12 inches in length.
Upon observing this orchid, Charles Darwin, based upon the theory of evolution, made a startling statement: There must be a moth with a proboscis of 11-12 inches!
No such creature had ever been seen or even imagined.
So, Darwin was making a truly outlandish prediction!
(Twenty-one years after Darwin’s death, such a moth was found in Madagascar.)
To illustrate the fallacy of making deterministic statements about ultimate cause, let’s place Darwin’s prediction within the format of a syllogism (in a very abbreviated form):
Premise: If evolution, then moth with long proboscis.
Observation: moth with long proboscis.
Conclusion: evolution.
You can see the logical fallacy. We have affirmed the consequent (moth with long proboscis) to conclude that an unobservable cause (evolution) is valid.
Because there might be other explanations for a moth with a long proboscis, we need to avoid affirming the consequent when investigating reality.
The solution is to use the format of the research and null hypotheses.
What Darwin was saying, to use the format of the research hypothesis (Ha), is:
There is a relationship between physical environment and animal physiology.
or: The orchid (environment) must have a moth
that fits it and can pollinate it (physiology).
To test this research hypothesis, however, would affirm the consequent.
Therefore, scientists test the null hypothesis.
The null hypothesis (Ho) is a statement that asserts no relationship between two variables.
There is no relationship between physical environment and animal physiology.
If the null hypothesis is rejected—if there appears to be a relationship between physical environment and animal physiology—then the scientist infers support for the research hypothesis that there is a relationship between physical environment and animal physiology.
If the null hypothesis is not rejected, then the research hypothesis is falsified; it is shown to be false.
If the null hypothesis is rejected—if there appears to be a relationship between physical environment and animal physiology—then the scientist infers support for the research hypothesis that there is a relationship between physical environment and animal physiology.
If the null hypothesis is not rejected, then the research hypothesis is falsified; it is shown to be false.
Recognize that because science cannot verify an hypothesis, is it also impossible to verify the falsification of an hypothesis.
Science therefore must rely upon probabilistic statements rather than deterministic ones.
Science therefore tests hypotheses within a margin of error, which is called the Type-I error. The social sciences typically use a margin of error equal to 5%.
We have described four requirements for an epistemology to be defined as science:
Phenomena of interest must be observable,
Hypotheses must be tested in the null form,
Conclusions are stated as probabilistic—within a margin of error.
The scientist must specify conditions under which the theory being tested can be falsified.
The implications of these requirements are that science can never confirm that a theory accurately represents reality, even if no counter-examples to the theory are ever found.
Observed relationships might be the result of the assertions of the theory.
Or, they might occur for some other reason.
For example, science can never confirm that evolution is the ultimate cause of the relationship between physical environment and animal physiology, even if no counter-examples are ever found.
The relationship might be the result of evolution.
Or, it might be the result of some other cause, such as intelligent design, for example.
Intelligent design is the claim that "certain features of the universe and of living things are best explained by an intelligent cause, not an undirected process such as natural selection."
It is a modern form of the traditional teleological argument for the existence of God, modified to avoid specifying the nature or identity of the designer.
Recall, however, that a requirement of science is that theories must be capable of being falsified by observing counter-examples.
When Darwin predicted the existence of a moth with a proboscis of 11-12 inches in length, for example, he was willing to risk his theory on the outcome of finding this moth.
Now, consider this correct form of a syllogism:
Premise: If A, then B.
Observation: A.
Conclusion: Then B.
