Good. This is an important distinction. I think that what you say could be rephrased in the following terms. An argument consists of a) a number of premises P1,...,Pn and b) an implicit or explicit claim to the effect that if P1,...,Pn are true, then that raises the probability that the conclusion C is true to X (or, alternatively, raises the probability of C with a certain value computed from the prior probability Y of C—e.g. Y + Z, where Z is some value).
In the case of deductive arguments, then X=1 (since if the premises are true in a deductive argument, then the conclusion is certainly true). When it comes to inductive arguments, X<1, however.
I think that generally, we are better at assessing a) - whether the premises are true—than b) - what the probability that the conclusion is true is given that the premises are true. An important reason for this is, I think, that in order to understand how the truth of the premises affect the probability of the conclusions, we need to have a comprehensive understanding of and overview over the whole question, whereas we normally do not have to do that in order to judge whether the premises are true. In other words, b) is normally a hard “holistic” judgment, a) normally an easier “atomistic” judgment.
For instance, say that someone says that “this article uses statistical technique A whereas statistical technique B is standard in the field. That clearly makes the conclusions of the article untrustworthy”. In this case, it is easy to check whether the premises are true—whether the article uses technique A and the standard in the field is technique B. However, in order to assess whether that indeed makes the conclusions untrustworthy, you need to have a good grasp of the relative reliabilities of techniques A and B, especially with regards to the specific methodology that the authors have used, and the conclusions they have arrived at.
In general, I think one should be quite explicit about the relevance of arguments. I think the story in the beginning of the post nicely illustrates that. Failure to be explicit about the relevance of arguments is a major problem within the academia, too—nit-picky arguments are often given far too much weight whereas more complicated but more significant arguments are unjustly ignored.
In the case of deductive arguments, then X=1 (since if the premises are true in a deductive argument, then the conclusion is certainly true).
Bringing up the case of deductive arguments made me realize that the Tortoise’s argument to Achilles seems like a case of relevance claims being used… creatively.
I really liked your post on inferential silence, too. I’d be interested in reading more from you on argumentation, and in particular how we can use feedback to improve argumentation. It’s a really important and somewhat neglected topic.
Good. This is an important distinction. I think that what you say could be rephrased in the following terms. An argument consists of a) a number of premises P1,...,Pn and b) an implicit or explicit claim to the effect that if P1,...,Pn are true, then that raises the probability that the conclusion C is true to X (or, alternatively, raises the probability of C with a certain value computed from the prior probability Y of C—e.g. Y + Z, where Z is some value).
In the case of deductive arguments, then X=1 (since if the premises are true in a deductive argument, then the conclusion is certainly true). When it comes to inductive arguments, X<1, however.
I think that generally, we are better at assessing a) - whether the premises are true—than b) - what the probability that the conclusion is true is given that the premises are true. An important reason for this is, I think, that in order to understand how the truth of the premises affect the probability of the conclusions, we need to have a comprehensive understanding of and overview over the whole question, whereas we normally do not have to do that in order to judge whether the premises are true. In other words, b) is normally a hard “holistic” judgment, a) normally an easier “atomistic” judgment.
For instance, say that someone says that “this article uses statistical technique A whereas statistical technique B is standard in the field. That clearly makes the conclusions of the article untrustworthy”. In this case, it is easy to check whether the premises are true—whether the article uses technique A and the standard in the field is technique B. However, in order to assess whether that indeed makes the conclusions untrustworthy, you need to have a good grasp of the relative reliabilities of techniques A and B, especially with regards to the specific methodology that the authors have used, and the conclusions they have arrived at.
In general, I think one should be quite explicit about the relevance of arguments. I think the story in the beginning of the post nicely illustrates that. Failure to be explicit about the relevance of arguments is a major problem within the academia, too—nit-picky arguments are often given far too much weight whereas more complicated but more significant arguments are unjustly ignored.
Thanks, I like your rephrasing.
Bringing up the case of deductive arguments made me realize that the Tortoise’s argument to Achilles seems like a case of relevance claims being used… creatively.
Hehe, creatively indeed.
I really liked your post on inferential silence, too. I’d be interested in reading more from you on argumentation, and in particular how we can use feedback to improve argumentation. It’s a really important and somewhat neglected topic.
Thanks!
(Thought I’d replied to this earlier but apparently I hadn’t.)