Andstæðingar EB á Íslandi og annarstaðar státa af óheiðarlegum málflutning, útúrsnúningum og lygum. Hérna er stutt yfirlit yfir þær rökvillur sem ég hef séð hjá andstæðingum EB á Íslandi. Ég bendi einnig á þá staðreynd að margir andstæðingar EB á Íslandi hafa ekki einu sinni heiðarleika til þess að vera heiðarlegir í umræðunni og leyfa frjáls skoðannaskipti á bloggsíðum sínum. Þess í stað eru athugasemdir flokkaðar og allar þær athugasemdir sem afsanna eða skemma málflutning viðkomandi andstæðings EB ritskoðaðar þannig að þær birtast aldrei. Ég hef fjallað áður um umrædda andstæðinga EB sem stunda þessa óheiðarlegu hegðun á blog.is og annarstaðar.
Hérna eru nokkrar algengar rökvillur andstæðinga EB.
When arguing with someone in an attempt to get at an answer or an explanation, you may come across a person who makes logical fallacies. Such discussions may prove futile. You might try asking for evidence and independent confirmation or provide other hypothesis that give a better or simpler explanation. If this fails, try to pinpoint the problem of your arguer’s position. You might spot the problem of logic that prevents further exploration and attempt to inform your arguer about his fallacy. The following briefly describes some of the most common fallacies:
appeal to ignorance (argumentum ex silentio) appealing to ignorance as evidence for something. (e.g., We have no evidence that God doesn’t exist, therefore, he must exist. Or: Because we have no knowledge of alien visitors, that means they do not exist). Ignorance about something says nothing about its existence or non-existence.
argument from adverse consequences: (e.g., We should judge the accused as guilty, otherwise others will commit similar crimes) Just because a repugnant crime or act occurred, does not necessarily mean that a defendant committed the crime or that we should judge him guilty. (Or: disasters occur because God punishes non-believers; therefore, we should all believe in God) Just because calamities or tragedies occur, says nothing about the existence of gods or that we should believe in a certain way.
argumentum ad baculum: An argument based on an appeal to fear or a threat. (e.g., If you don’t believe in God, you’ll burn in hell)
argumentum ad ignorantiam: A misleading argument used in reliance on people’s ignorance.
argumentum ad populum: An argument aimed to sway popular support by appealing to sentimental weakness rather than facts and reasons.
circular reasoning: stating in one’s proposition that which one aims to prove. (e.g. God exists because the Bible says so; the Bible exists because God influenced it.)
Þessi rökvilla er mikið notuð af andstæðingum EB á Íslandi. Þeir velja það sem hentar þeim og henda restinni.
confirmation bias (similar to observational selection): This refers to a form of selective thinking that focuses on evidence that supports what believers already believe while ignoring evidence that refutes their beliefs. Confirmation bias plays a stronger role when people base their beliefs upon faith, tradition and prejudice. For example, if someone believes in the power of prayer, the believer will notice the few „answered“ prayers while ignoring the majority of unanswered prayers (which would indicate that prayer has no more value than random chance at worst or a placebo effect, when applied to health effects, at best).
excluded middle (or false dichotomy): considering only the extremes. Many people use Aristotelian either/or logic tending to describe in terms of up/down, black/white, true/false, love/hate, etc. (e.g., You either like it or you don’t. He either stands guilty or not guilty.) Many times, a continuum occurs between the extremes that people fail to see. The universe also contains many „maybes.“
half truths (suppressed evidence): An statement usually intended to deceive that omits some of the facts necessary for an accurate description.
Þessar rökvillur eru mikið notaðar, þó minna en sú sem nefndi hérna að ofan.
misunderstanding the nature of statistics: (e.g., the majority of people in the United States die in hospitals, therefore, stay out of them.) „Statistics show that of those who contract the habit of eating, very few survive.“ — Wallace Irwin
Þessi rökvilla er mikið notuð af þessum hérna aðila.
observational selection (similar to confirmation bias): pointing out favorable circumstances while ignoring the unfavorable. Anyone who goes to Las Vegas gambling casinos will see people winning at the tables and slots. The casino managers make sure to install bells and whistles to announce the victors, while the losers never get mentioned. This may lead one to conclude that the chances of winning appear good while in actually just the reverse holds true.
Þessi rökvilla er mikið notuð af andstæðingum EB.
statistics of small numbers: similar to observational selection (e.g., My parents smoked all their lives and they never got cancer. Or: I don’t care what others say about Yugos, my Yugo has never had a problem.) Simply because someone can point to a few favorable numbers says nothing about the overall chances.
Fake Precision
Example:
Sometimes [a] big ado is made about a difference that is mathematically real and demonstrable but so tiny as to have no importance. … A case in point is the hullabaloo over practically nothing that was raised so effectively, and so profitably, by the Old Gold cigarette people.
It started innocently with the editor of the Reader’s Digest, who smokes cigarettes but takes a dim view of them all the same. His magazine went to work and had a battery of laboratory folk analyze the smoke from several brands of cigarettes. The magazine published the results, giving the nicotine and whatnot content of the smoke by brands. The conclusion stated by the magazine and borne out in its detailed figures was that all the brands were virtually identical and that it didn’t make any difference which one you smoked. …But somebody spotted something. In the lists of almost identical amounts of poisons, one cigarette had to be at the bottom, and the one was Old Gold. …[B]ig advertisements appeared in newspapers at once in the biggest type at hand. The headlines and the copy simply said that of all cigarettes tested by this great national magazine Old Gold had the least of these undesirable things in its smoke.
Unrepresentative Sample
Exposition:
This is a fallacy affecting statistical inferences, which are arguments of the following form:
N% of sample S has characteristic C.
(Where sample S is a subset of set P, the population.)
Therefore, N% of population P has characteristic C.For example, suppose that an opaque bag is full of marbles, and you can win a prize by guessing the proportions of colors of the marbles in the bag. Assume, further, that you are allowed to stick your hand into the bag and withdraw one fistful of marbles before making your guess. Suppose that you pull out ten marbles, six of which are black and four of which are white. The set of all marbles in the bag is the population which you are going to guess about, and the ten marbles that you removed is the sample. You want to use the information in your sample to guess as closely as possible the proportion of colors in the bag. You might draw the following conclusions:
* 60% of the marbles in the bag are black.
* 40% of the marbles in the bag are white.Notice that if 100% of the sampled marbles were black, say, then you could infer that all the marbles in the bag are black, and that none of them are white. Thus, the type of inference usually referred to as „induction by enumeration“ is a type of statistical inference, even though it doesn’t use percentages. Similarly, from the example we could just draw the vague conclusion that most of the marbles are black and few of them are white.
The strength of a statistical inference is determined by the degree to which the sample is representative of the population, that is, how similar in the relevant respects the sample and population are. For example, if we know in advance that all of the marbles in the bag are the same color, then we can conclude that the sample is perfectly representative of the color of the population—though it might not represent other aspects, such as size. When a sample perfectly represents a population, statistical inferences are actually deductive enthymemes. Otherwise, they are inductive inferences.
Moreover, since the strength of statistical inferences depend upon the similarity of the sample and population, they are really a species of argument from analogy, and the strength of the inference varies directly with the strength of the analogy. Thus, a statistical inference will commit the Fallacy of Unrepresentative Sample when the similarity between the sample and population is too weak to support the conclusion. There are two main ways that a sample can fail to sufficiently represent the population:
1. The sample is simply too small to represent the population, in which case the argument will commit the subfallacy of Hasty Generalization.
2. The sample is biased in some way as a result of not having been chosen randomly from the population. The Example is a famous case of such bias in a sample. It also illustrates that even a very large sample can be biased; the important thing is representativeness, not size. Small samples can be representative, and even a sample of one is sufficient in some cases.
One-Sidedness
Example:
You’ve spoke about having seen the children’s prisons in Iraq. Can you describe what you saw there?
The prison in question is at the General Security Services headquarters, which was inspected by my team in Jan. 1998. It appeared to be a prison for children—toddlers up to pre-adolescents—whose only crime was to be the offspring of those who have spoken out politically against the regime of Saddam Hussein. It was a horrific scene. Actually I’m not going to describe what I saw there because what I saw was so horrible that it can be used by those who would want to promote war with Iraq, and right now I’m waging peace.
Source: Massimo Calabresi, „Scott Ritter in His Own Words“, Time, 9/14/2002
Exposure:
During every election, there are news stories claiming that one candidate is ahead of another based upon poll results. However, in the small print of most polls you will notice that the polling numbers have a margin of error of plus-or-minus three percentage points. This means that the poll results are really a range of possible percentages. Suppose, for instance, that the following is the result of the most recent poll:
Candidate D: 44% Candidate R: 39%It looks as if Candidate D is ahead of Candidate R, but the margin of error means that the range of percentages is:
Candidate D: 41-47% Candidate R: 36-42%In other words, Candidate R might actually be ahead of Candidate D, 42% to 41%. Because of the imprecision of most poll results, one candidate must be at least six percentage points ahead of the other to be truly in the lead.
Case Study: How to Read a Poll
Fleiri rökvillur er að finna í málflutningi andstæðinga EB. Ég ætla hinsvegar að láta þetta duga að sinni.
Hægt er að lesa um þessar rökvillur á þessum hérna vefsíðum. Stjórnmálamenn á Íslandi nota rökvillur í bílförmum. Sérstaklega í ástandinu eins og það er í dag.