Bad Arguments: 100 of the Most Important Fallacies in Western Philosophy by Robert Arp

Bad Arguments: 100 of the Most Important Fallacies in Western Philosophy Reviews

• 
  Islomjon

  "Bad Arguments" is an outstanding collection of the most important fallacies. Editors of the book concisely explain the overall logic in the introduction and it was easy to follow their ideas on formal fallacies with philosophical terminologies. In addition, illustrious epigraphs in every chapter make thing more clearer to understand about particular fallacies. I appreciated the tone of the book, its wide content in politics, mass media and ordinary daily routines. It turns out to be that fallacies are all around us, and we need to precisely differentiate them, because they can lead to be fooled around which most of us are inadvertently. This book teaches how to detect, avoid and infer logical fallacies.
• 
  Eoin Barry

  I would not recommend this to someone completely unversed in the field of logic or logical fallacies. Ultimately there are issues between each chapter with some introducing their own nomenclature. As well as this it seems some chapters fall into fallacies introduced elsewhere in the book.
• 
  Chuck Kollars

  What I desired to know I learned simply from the Table of Contents: of all the "tricks" used in disputation, only a small portion can be found with mathematical/symbolic logic. Reading, I discovered that most of these have been known and named ever since Greek times.
  
  There are several books with very similar titles ...but the numbers differ: "the 50 most important fallacies", "the 30 most important fallacies", etc. So -given the larger number "100" here- I expected the last part of the book to be arcane/irrelevant/impenetrable, and sure enough it was. Especially the last twenty or so items seem written mostly to impress other academics in the field. I often had the feeling I was eavesdropping on academics "splitting hairs" for each other, or worse that I was stuck in a nightmare where I had to take the college Philosophy 101 course over and over.
  
  Each "logic error" takes 2-4 pages; they were written by many different authors, seemingly for many different purposes and audiences. There's unfortunately far too little consistency in the _content of each item. Some focus on an example, while others don't even include an example. Some give the academic history of the item, others don't. Quite a few cross-reference other items, claiming to be "similar" or "a subset". Unfortunately the references aren't coordinated, so sometimes B will claim to be a subset of A while A claims to have no subsets.
  
  My takeaways were: 1] the working definitions of most "fallacies" are pretty muddy, so politicians can often spin the truth mightily yet claim they're not spinning at all; and 2] there's no substitute for listening carefully and thinking deeply (i.e. using "mathematical logic" as a shortcut to identify tricks doesn't work).
• 
  Sami

  Rarely has the expression “mixed bag” so accurately reflected my opinion of a book. There is definitely some good information in there, but the quality varies hugely from one chapter to the next, such that I could only ever recommend the book with reservations if at all.
  
  I am not going to discuss the entirety of the 100 chapters but perhaps it would make sense to discuss a few of them.
  
  I really appreciate that the first few informal fallacies are also presented with a discussion of when they may not be fallacious, which is a worthwhile question to think about.
  
  However, by the time we reach the chapter on the “genetic fallacy”, this approach has been completely thrown out the window and we are left with a largely rambling yet unnuanced chapter which uncritically states: “Neither the origin of a claim nor the process of its genesis determines the truth‐value of the claim. Just as strong feelings of dislike for the truth of a claim do not make the claim false, strong feelings regarding a claim’s origin do not change its truth‐value.”
  
  This fails to point out that mathematically, if, C having occurred, we learn that one of its possible causes A has also occurred, this should lower the probability that we assign to its other possible cause B, compared to when we knew only that C had occurred and not A. (Judea Pearl, 1988, “
  Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference”, 2.2.4 Multiple Causes and “Explaining Away”). For example, if we take C = “Andrew Wakefield claims that vaccines cause autism”, learning that A = “he was paid to falsely make that claim with a fake study” is true should lower the probability of B = “vaccines do cause autism”. Likewise, learning that the reason for C = “acupuncture is popular” is A = “outright propaganda” lowers the probability that C is due to B = “acupuncture works”. This has nothing to do with “strong feelings or dislike”. For more discussion on this “fallacy”:
  https://blog.apaonline.org/2021/06/24...
  
  Another odd case is the trio of the “naturalistic fallacy”, “moralistic fallacy” and “is/ought fallacy” chapters. In my understanding, it is quite common for “naturalistic fallacy” to refer to the is/ought fallacy (i.e. deriving an “ought” from an “is”), of which the moralistic fallacy is considered the inverse (deriving an “is” from an “ought”). Here, instead, we get three separate chapters, by three distinct authors. I might have been interested in finding out what distinction the first author made between the naturalistic fallacy and the is/ought fallacy, except I couldn’t actually figure out what their point was. The chapter on the moralistic fallacy was much clearer and could arguably have replaced the three of them altogether. The “is/ought fallacy” chapter, then, was extremely strange, in that it starts by renaming the fallacy to a “doctrine” right away, and then proceeds to use a very bizarre argument to justify that move: first, it sets up the straw man that the fallacy is about the impossibility to derive any “ought” from any “is” at all, and then it attacks that straw man by means of a question-begging syllogism.
  
  The chapter on the base rate fallacy was somewhat disappointing as well. It is an important topic that would have deserved a good exposition, but the chapter contains various errors, such as defining “sensitivity” in a way that matches the definition of the negative predictive value, and “selectivity” (not the more standard term “specificity”?) as if it were the positive predictive value. It then goes on to state that a true-to-false positive ratio of 3:1 means that a positive result is true in only two out of three cases, instead of the correct three out of four.
  
  More benignly, the example chosen by the “hasty generalization” chapter was a bit dubious: it is argued that if, having graded 10 quizzes out of 25, a teacher notices that all 10 got a certain question wrong, then it is justified to assume that the rest of them also got it wrong, because 10 out of 25 is a sufficiently large sample. In contrast to that, it is then stated that if the teacher were to grade 22 out of 100 quizzes and none of those 22 got higher than D, then it would be hasty to believe that no one will get at least B on that quiz, because 22 is not a sufficiently large sample to generalise about a group of 100. But an actual probabilistic analysis of both cases using a flat prior actually shows that the probability distributions for the proportion of quizzes that would (a) get the question wrong in the 10/25 case, and (b) receive ≤D (not even ≤C!) in the 22/100 case are actually quite similar – the latter is not that much hastier. (The posterior probability distributions for the absolute numbers are respectively 10 + BetaBin(25 − 10, 10 + 1, 1) and 22 + BetaBin(100 − 22, 22 + 1, 1).)
  
  This illustrates a common tendency I have noticed to overestimate the importance of the size N of the larger population when estimating the proportion that matches a certain criterion by sampling n members and observing k “successes”. While it is true that, if N=n (we have tested the entire population), the proportion is known with certainty to be exactly k/n, it does not follow that, as N grows towards infinity, our estimate would become infinitely vague, as some people seem to believe. The uncertainty on the absolute number K of successes would grow, but the estimate of the relative proportion K/N could still be quite precise. (This is why tasting soup to determine whether it has enough salt works without having to drink the entire pot.) Still using a flat prior, the posterior for the proportion would tend to Beta(k + 1, n − k + 1).
  
  To end my review on a more positive note: I was quite pleased by the chapter about the gambler’s fallacy. It is quite well explained, to the point, with clear examples and without slipping where it would have been easy to do so.
• 
  Trevor

  Stellar guide to logic, good argument and bad arguments.
  Very accessible, and easy to follow.
  I’m currently completing a PhD and analysing argument in my interviews and this provides such a simple guide to all of the argument I had heard but did not have the language or framework to picture and effectively critique.
  
  Great job, guys and gals, thanks!
• 
  Ulzeta

  Very informative. A must read.
• 
  Jay Merrill

  Took some time to get through it with clarity, but 100% worth the effort. Really challenged my thinking.