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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: 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.

myice

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”.

myice

Logical Fallacies: Appeal to Authority
An appeal to an authority is an argument that attempts to establish its conclusion by citing a perceived authority who claims that the conclusion is true.
In all cases, appeals to authority are fallacious; no matter how well-respected someone is, it is possible for them to make a mistake. The mere fact that someone says that something is true therefore doesn’t prove that it is true.
The worst kinds of appeal to authority, however, are those where the alleged authority isn’t an authority on the subject matter in question. People speaking outside of their area of expertise certainly aren’t to be trusted on matters of any importance without further investigation.
Example
“Darwin’s theory of evolution is false; my pastor says so.”
A pastor saying that a complex scientific theory is false doesn’t prove that it’s so, particularly if the pastor lacks a background in science

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Logical Fallacies: Appeal to History
There are two types of appeal to history. The first is committed by arguments that use past cases as a guide to the future. This is the predictive appeal to history fallacy. Just because something has been the case to date, doesn’t mean that it will continue to be the case.
This is not to say that we can’t use the past as a guide to the future, merely that predictions of the future based on the past need to be treated with caution.
The second type of appeal to history is committed when it is argued that because something has been done a particular way in the past, it ought to be done that way in the future. This is the normative appeal to history fallacy, the appeal to tradition. The way that things have always been done is not necessarily the best way to do them. It may be that circumstances have changed, and that what used to be best practice is no longer. Alternatively, it may be that people have been consistently getting it wrong in the past. In either case, using history as a model for future would be a mistake.
Example
At the start of the 2006 Premiership season, some might have argued, “Under Jose Mourinho, Chelsea have been unstoppable in the Premiership; the other teams might as well give up on the league now and concentrate on the Cup competitions.”

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Logical Fallacies: Appeal to Popularity
The appeal to popularity fallacy is the fallacy of arguing that because lots of people believe something it must be true. Popular opinion is not always a good guide to truth; even ideas that are widely accepted can be false.
Example
“Pretty much everyone believes in some kind of higher power, be it God or something else. Therefore atheism is false.”
Logical Fallacies: Circularity
Circular arguments are arguments that assume what they’re trying to prove. If the conclusion of an argument is also one of its reasons, then the argument is circular.
The problem with arguments of this kind is that they don’t get you anywhere. If you already believe the reasons offered to persuade you that the conclusion is true, then you already believe that the conclusion is true, so there’s no need to try to convince you.
If, on the other hand, you don’t already believe that the conclusion is true, then you won’t believe the reasons given in support of it, so won’t be convinced by the argument.
In either case, you’re left believing exactly what you believed before. The argument has accomplished nothing.

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Example
“You can trust me; I wouldn’t lie to you.”
Logical Fallacies: Confusing Necessary and Sufficient Conditions
Necessary conditions are conditions which must be fulfilled in order for an event to come about. It is impossible for an event to occur unless the necessary conditions for it are fulfilled. For example, a necessary condition of you passing your A-level Critical Thinking is that you enroll on the course. Without doing so, there’s no way that you can get the qualification.
Sufficient conditions are conditions which, if fulfilled, guarantee that an event will come to pass. It is impossible for an event not to occur if the sufficient conditions for it are fulfilled. For example, a sufficient condition of you passing AS Critical Thinking is that you get enough marks on the two exams. If you do that, there’s no way that you can fail.
Some arguments confuse necessary and sufficient conditions. Such arguments fail to prove their conclusions.
Example
“People who don’t practice regularly always fail music exams. I’ve practiced regularly though, so I’ll be all right.”
Not having practiced regularly may be a sufficient condition for failing a music exam, but it isn’t necessary. People who have practiced regularly may fail anyway, due to nerves, perhaps, or simply a lack of talent.

myice

Logical Fallacies: Correlation not Causation
The correlation not causation fallacy is committed when one reasons that just because two things are found together (i.e. are correlated) there must be a direct causal connection between them. Often arguments of this kind seem compelling, but it’s important to consider other possible explanations before concluding that one thing must have caused the other.
Example
“Since you started seeing that girl your grades have gone down. She’s obviously been distracting you from your work, so you mustn’t see her anymore.”
Logical Fallacies: Inconsistency
If, in the course of an argument, an arguer contradicts himself or herself, then we need not accept their conclusion. This is because if an argument rests on contradictory claims, then at least one of those claims must be false, and false claims prove nothing.
Example
“Murder is the worst crime that there is. Life is precious; no human being should take it away. That’s why it’s important that we go to any length necessary to deter would-be killers, including arming the police and retaining the death penalty.”
This argument both affirms that no human being should take the life of another, and that we should retain the death penalty. Until this inconsistency is ironed out of the argument, it won’t be compelling.

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Logical Fallacies: Generalisation
Generalisations draw conclusions from insufficient evidence. In order for a set of evidence to support and general conclusion, the evidence must be drawn from a sufficient number of cases, and from a sufficiently varied set of cases. The more limited or unrepresentative the evidence sample, the less convincing an argument will be.
Example
“Smoking isn’t bad for you; my grandad smoked thirty a day for his whole life and lived to be 92.”
Logical Fallacies: Restricting the Options
We are sometimes faced with a number of possible views or courses of action. By a process of elimination, we may be able to eliminate these options one-by-one until only one is left. We are then forced to accept the only remaining option. Arguments that do this, but fail to consider all of the possible options, excluding some at the outset, commit the restricting the options fallacy.
Example
“Many gifted children from working class backgrounds are let down by the education system in this country. Parents have a choice between paying sky-high fees to send their children to private schools, and the more affordable option of sending their children to inferior state schools. Parents who can’t afford to pay private school fees are left with state schools as the only option. This means that children with great potential are left languishing in comprehensives“.
Quite apart from any problems with the blanket dismissal of all comprehensives as inferior, this argument fails to take into account all of the options available to parents. For the brightest students, scholarships are available to make private school more affordable, so there is a third option not considered above: applying for scholarships to private schools. Unless this option can be eliminated, e.g. by arguing that there are too few scholarships for all gifted children to benefit from them, along with other options such as homeschooling, the conclusion that children with great potential have no alternative but to go to comprehensives is unproven.

myice

Logical Fallacies: Slippery Slope
Sometimes one event can set of a chain of consequences; one thing leads to another, as the saying goes. The slippery slope fallacy is committed by arguments that reason that because the last link in the chain is undesirable, the first link is equally undesirable.
This type of argument is not always fallacious. If the first event will necessarily lead to the undesirable chain of consequences, then there is nothing wrong with inferring that we ought to steer clear of it. However, if it is possible to have the first event without the rest, then the slippery slope fallacy is committed.
Example
“If one uses sound judgement, then it can occasionally be safe to exceed the speed limit. However, we must clamp down on speeding, because when people break the law it becomes a habit, and escalates out of control. The more one breaks the law, the less respect one has for it. If one day you break the speed limit, then the next you’ll go a little faster again, and pretty soon you’ll be driving recklessly, endangering the lives of other road-users. For this reason, we should take a zero-tolerance approach to speeding, and stop people before they reach dangerous levels.”