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Logical Fallacies
Logical Fallacies: Ad Hominem
“Ad hominem” is Latin for “against the man”. The ad hominem fallacy is the fallacy of attacking the person offering an argument rather than the argument itself.
Ad hominems can simply take the form of abuse: e.g. “don’t listen to him, he’s a jerk”. Any attack on irrelevant biographical details of the arguer rather than on his argument counts as an ad hominem, however: e.g. “that article must be rubbish as it wasn’t published in a peer-reveiwed journal”; “his claim must be false as he has no relevant expertise”; “he says that we should get more exercise but he could stand to lose a few pounds himself”.

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Evidence Sampling: Insufficient Data
A common problem with evidence sampling is drawing conclusions from insufficient data. This is related to the generalisation fallacy.
To prove a theory, it is not enough to observe a couple of instances that seem to support it. If we want to know what percentage of the population take holidays abroad, we can’t find out by asking five people, calculating the percentage, and applying the result to the population as a whole. We need more data.
This raises the question: how much data is enough? At what point does a data-set become sufficiently large to draw conclusions from it?
Of course, having enough data is not a black-or-white affair; there is no magic number of observations which, when reached, means that any conclusion drawn is adequately supported. Rather, sufficiency of data is a matter of degree; the more evidence the better. The amount of confidence that we can have in an inference grows gradually as more evidence is brought in to support it.
Evidence Sampling: Unrepresentative Data
Simply having enough data is not enough to guarantee that a conclusion drawn is warranted; it is also important that the data is drawn from a variety of sources and obtained under a variety of different conditions.
A survey of voting intentions conducted outside the local Conservative Club is not going to provide an accurate guide to who is going to win the next general election. A disproportionate number of people in the vicinity will be Conservative voters, and so the results of the survey will be skewed in favour of the Tory party. The sample is not representative.
A survey to find out what proportion of the population own mobile phones would be similarly (though less obviously) flawed if it were conducted near a Sixth-Form College. The sample of the population would be skewed towards teenagers, who are more likely than average to own mobile phones, distorting the figures.
Collecting data from a variety of sources is one thing; collecting it under a variety of conditions is another. A survey of what type of vehicles use local roads conducted at a variety of locations, but always at the same time of day, would not yield representative data. Conducting it during rush-hour would mean that commuter-traffic would be over-represented in the results; conducting it in the evenings might mean that public transport would under-represented in the results. Differences in what types of drivers drive at what times would need to be factored in when designing the experiment.
The quality of a data-set is thus not just a matter of how much data it contains, but also of how representative that data is likely to be. To minimise the problem of unrepresentative data, evidence must be collected from as wide a range of sources as possible, and under as varied conditions as possible.

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Evidence Sampling: Misrepresenting the Data
Perhaps the most basic error in the use of empirical data is simply misrepresenting it. This can occur in a number of ways.
One possibility is simply deliberate distortion, claiming that a data set proves something when it doesn’t. If people have an agenda, and set out to prove it, they may reach for the first bit of evidence they can find that even seems to fit their position. Closer examination may show that the evidence isn’t quite as supportive as was first claimed. Alternatively, someone confronted with potentially problematic evidence for their position may misrepresent it to make the problem go away.
A similar error can be committed accidentally. Sometimes when people look at a data-set they see what they want or expect to see, rather than what is actually there. The effect of our presuppositions on our interpretation of evidence should not be under-estimated. It can lead to conclusions being drawn which simply aren’t supported by the evidence.
A further way in which data may be misrepresented is if it is presented selectively. A varied data set can be described focusing in on certain sections of it. The data set as a whole is thus misrepresented; it is effectively replaced by a new set comprising of unrepresentative data.

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Evidence Sampling
When presented with observational evidence to support a claim, we need to be wary. It is said that there are lies, damned lies, and statistics, and that you can devise a survey to prove anything that you want. If we are told “A study has shown that...” then we should think twice before we accept the conclusion that is drawn from it.
Misrepresenting the Data
The most basic mistake in interpreting evidence is simply misrepresenting the data. If the observational data do not fit the inference drawn, then there is a problem. There is the possibility of deliberate distortion, accidental misinterpretation, and selectivity.
Insufficient Data
A more common error is drawing a conclusion from insufficient data. Every study has a margin of error, and the smaller the study the greater this will be. Studies with a significant margin of error always leave doubt about any conclusions based on them, so it is important to consider the quantity of data in a study in assessing its validity.
Unrepresentative Data
A constant danger in empirical studies is unrepresentative data. A study that has a sufficient quanity of data may nevertheless be flawed due to insufficient quality of evidence. For a general conclusion to be drawn with any confidence from a limited data set, it must be reasonable to believe that the data set is representative.

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Elements of Arguments: Indicator Words
There are certain words that often indicate the presence of a particular element of an argument. These are called indicator words. Not every element of an argument is flagged up by an indicator word, and not every use of an indicator word is associated with an element of an argument, but indicator words are useful guides.
Conclusions
Conclusions are often indicated by one of the following words or phrases: “therefore”; “thus”; “hence”; “so”; “in conclusion”; “consequently”; “showing that”; “demonstrating that”; “proving that”; “establishing that”; “meaning that”; “entails that”; “implies that”; “as a result”. In past papers, conclusions have often been statements about how we ought to respond to something, so “should”, “must”, and “ought to” should also be treated as indicator words, albeit cautiously.
Reasons
Indicator words for reasons include the following: “because”; “as”; “since”; “in order to”; “otherwise”. Sometimes authors enumerate their reasons, writing “First, ...”, “Second, ...”, “Third, ...” etc., which can also help in their identification.
Counter-Arguments
Counter-arguments can be given away by phrases like “some might argue that”, “it has been suggested that”, or equivalent phrases. The main giveaway for counter-arguments, though, is that the reason(s) cited count against the author’s conclusion rather than for it.

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Elements of Arguments: Counter-Arguments
A counter-argument is an argument that goes against the author’s main conclusion. Typically, counter-arguments are considered and rejected in an attempt to strengthen the author’s case.
For example, “If Superman and Spiderman had a fight, then Superman would win as his ability to fly would mean he could attack from any angle. You might think that Spiderman’s ability to hurl webs (a ranged weapon) would give him the edge, but Superman would be manoeuvrable enough to dodge them.”
In this passage, the main conclusion is “If Superman and Spiderman had a fight, then Superman would win”. The reason given in support of this is “his ability to fly would mean he could attack from any angle”.
Also considered, though, is an argument that Spiderman would win: “Spiderman’s ability to hurl webs (a ranged weapon) would give him the edge”; this is a counter-argument.
It is then dismissed with the comment “Superman would be manoeuvrable enough to dodge them”.

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Elements of Arguments: Assumptions
An assumption, for the purposes of A-level Critical Thinking, is an unstated reason. It is something that must be true for an argument to work, but which is not explicitly stated in the argument.
For example, the argument “The college address is the same street as I’m standing on; therefore, the college must be nearby” assumes that the street isn’t very long. If the street is long, then the college could be on it but still miles away.
The Negative Test
To test whether something is assumed by an argument, you can use the negative test. This involves inserting the opposite of the alleged assumption into the argument and seeing if it still makes sense.
For example, to test whether the above argument assumes that the street isn’t very long, we would check whether this makes sense: “The college address is the same street as I’m standing on; the street is very long; therefore, the college must be nearby.”
With the opposite of the alleged assumption inserted, the argument clearly doesn’t make sense, so the alleged assumption must be true for the argument to work; it is assumed by the original argument.
Don’t Quote!
When answering a question that asks you to identify an assumption, unlike when answering other questions, you should never give a quote from the text; by definition, assumptions are unstated.

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Elements of Arguments: Intermediate Conclusions
An intermediate conclusion is something in an argument that functions both as a reason and as a conclusion.
To function as a reason, it must offer support to the main conclusion of the argument (or to another intermediate conclusion).
To function as a conclusion, there must be something else in the argument that lends it support.
Take, for example, the following argument: “Your face is covered in chocolate, so it must have been you that ate my cake, so you owe me a cake.” The main conclusion of this argument is the final clause: “You owe me a cake.” This is supported by the previous clause, which is therefore functioning as a reason, “it must have been you that ate my cake.” This clause, though, is also supported by the previous clause, “Your face is covered in chocolate”, so it is both a conclusion and a reason; it is an intermediate conclusion.

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Elements of Arguments: Conclusions
The conclusion of an argument is the main point that it is trying to get you to accept. You’ll often (but not always) find this statement either at the beginning or the end of a passage. It may be indicated by a word such as “therefore”, “thus”, or even “in conclusion”.
The Therefore Test
A test that can help you to identify the conclusion of a passage is the ‘therefore test’. Simply insert the word “therefore” into the passage directly before the phrase that you think is the conclusion. If the passage makes sense, then you’ve probably got the right section. If it doesn’t, then you haven’t.
Quote!
When you’re asked to identify the conclusion of a passage, you must give a direct quotation. You’re being asked to pick out a specific phrase that the rest of the passage is trying to get you to accept. If you give a rough paraphrase, then you risk changing the meaning of the phrase slightly, and so giving an inaccurate statement of the conclusion. This can lose you marks. Even missing out a word or two can change the meaning of the conclusion resulting in inaccuracy in your answer. To err on the side of caution, always quote word-for-word.

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Elements of Arguments: Reasons
The reasons in an argument are the claims made in an attempt to persuade you that the conclusion is true.
The Because Test
A test that can help you to identify the reasons in a passage is the ‘because test’. Simply insert the word “because” into the passage directly before the phrase that you think is a reason. If the passage makes sense, then you’ve probably got the right section. If it doesn’t, then you haven’t.
Quote!
When you’re asked to identify the reasons in a passage, you must give direct quotations. You’re being asked to pick out a specific claims that the passage makes in attempting to prove its conclusion. If you give a rough paraphrase, then you risk changing the claim, resulting in inaccuracy in your answer and so losing you marks.