Easy - Medium - Hard
Changing difficulty modes will reset your session score.
Check the About page for difficulty details.

Play The Game, Reclaim Your Brain

Select the one clearest logical fallacy in the example,
then click the POP bubble below. New here?.. Check out the Fallacy List first.
"Politicians act in corrupt ways, so the only thing we can do about it is to abolish government completely."
Disagree with 'correct' answer?
Join the discussion.
[in new window]
Biased Generalization
AKA Biased Statistics, Loaded Sample, Prejudiced Statistics, Prejudiced Sample, Loaded Statistics, Biased Induction

Category: Fallacies of Presumption

This fallacy is committed when a person draws a conclusion about a population based on a sample that is biased or prejudiced in some manner. It has the following form:

  1. Sample S, which is biased, is taken from population P.
  2. Conclusion C is drawn about Population P based on S.
The person committing the fallacy is misusing the following type of reasoning, which is known variously as Inductive Generalization, Generalization, and Statistical Generalization:
  1. X% of all observed A's are B's.
  2. Therefore X% of all A's are B's.
The fallacy is committed when the sample of A's is likely to be biased in some manner. A sample is biased or loaded when the method used to take the sample is likely to result in a sample that does not adequately represent the population from which it is drawn.

Biased samples are generally not very reliable. As a blatant case, imagine that a person is taking a sample from a truckload of small colored balls, some of which are metal and some of which are plastic. If he used a magnet to select his sample, then his sample would include a disproportionate number of metal balls (after all, the sample will probably be made up entirely of the metal balls). In this case, any conclusions he might draw about the whole population of balls would be unreliable since he would have few or no plastic balls in the sample.

The general idea is that biased samples are less likely to contain numbers proportional to the whole population. For example, if a person wants to find out what most Americans thought about gun control, a poll taken at an NRA meeting would be a biased sample.

Since the Biased Sample fallacy is committed when the sample (the observed instances) is biased or loaded, it is important to have samples that are not biased making a generalization. The best way to do this is to take samples in ways that avoid bias. There are, in general, three types of samples that are aimed at avoiding bias. The general idea is that these methods (when used properly) will result in a sample that matches the whole population fairly closely. The three types of samples are as follows...

Random Sample: This is a sample that is taken in such a way that nothing but chance determines which members of the population are selected for the sample. Ideally, any individual member of the population has the same chance as being selected as any other. This type of sample avoids being biased because a biased sample is one that is taken in such a way that some members of the population have a significantly greater chance of being selected for the sample than other members. Unfortunately, creating an ideal random sample is often very difficult.

Stratified Sample: This is a sample that is taken by using the following steps: 1) The relevant strata (population subgroups) are identified, 2) The number of members in each stratum is determined and 3) A random sample is taken from each stratum in exact proportion to its size. This method is obviously most useful when dealing with stratified populations. For example, a person's income often influences how she votes, so when conducting a presidential poll it would be a good idea to take a stratified sample using economic classes as the basis for determining the strata. This method avoids loaded samples by (ideally) ensuring that each stratum of the population is adequately represented.

Time Lapse Sample: This type of sample is taken by taking a stratified or random sample and then taking at least one more sample with a significant lapse of time between them. After the two samples are taken, they can be compared for changes. This method of sample taking is very important when making predictions. A prediction based on only one sample is likely to be a Hasty Generalization (because the sample is likely to be too small to cover past, present and future populations) or a Biased Sample (because the sample will only include instances from one time period).

People often commit Biased Sample because of bias or prejudice. For example, a person might intentionally or unintentionally seek out people or events that support his bias. As an example, a person who is pushing a particular scientific theory might tend to gather samples that are biased in favor of that theory.

People also commonly commit this fallacy because of laziness or sloppiness. It is very easy to simply take a sample from what happens to be easily available rather than taking the time and effort to generate an adequate sample and draw a justified conclusion.

It is important to keep in mind that bias is relative to the purpose of the sample. For example, if Bill wanted to know what NRA members thought about a gun control law, then taking a sample at a NRA meeting would not be biased. However, if Bill wanted to determine what Americans in general thought about the law, then a sample taken at an NRA meeting would be biased.

Click For Fallacy Description
39
Appeal to Flattery
AKA Apple Polishing, various 'colorful' expressions

Category: Fallacies of Relevance (Red Herrings) → Distracting Appeals

An Appeal to Flattery is a fallacy of the following form:

  1. Person A is flattered by person B.
  2. Person B makes claim X.
  3. Therefore X is true.
The basic idea behind this fallacy is that flattery is presented in the place of evidence for accepting a claim. This sort of "reasoning" is fallacious because flattery is not, in fact, evidence for a claim. This is especially clear in a case like this: "My Bill, that is a really nice tie. By the way, it is quite clear that one plus one is equal to forty three."

Click For Fallacy Description
1
Ignoring a Common Cause
AKA Questionable Cause

Category: Fallacies of Presumption → Casual Fallacies

This fallacy has the following general structure:

  1. A and B are regularly connected (but no third, common cause is looked for).
  2. Therefore A is the cause of B.
This fallacy is committed when it is concluded that one thing causes another simply because they are regularly associated. More formally, this fallacy is committed when it is concluded that A is the cause of B simply because A and B are regularly connected. Further, the causal conclusion is drawn without considering the possibility that a third factor might be the cause of both A and B.

In many cases, the fallacy is quite evident. For example, if a person claimed that a person's sneezing was caused by her watery eyes and he simply ignored the fact that the woman was standing in a hay field, he would have fallen prey to the fallacy of ignoring a common cause. In this case, it would be reasonable to conclude that the woman's sneezing and watering eyes was caused by an allergic reaction of some kind. In other cases, it is not as evident that the fallacy is being committed. For example, a doctor might find a large amount of bacteria in one of her patients and conclude that the bacteria are the cause of the patient's illness. However, it might turn out that the bacteria are actually harmless and that a virus is weakening the person, Thus, the viruses would be the actual cause of the illness and growth of the bacteria (the viruses would weaken the ability of the person's body to resist the growth of the bacteria).

As noted in the discussion of other causal fallacies, causality is a rather difficult matter. However, it is possible to avoid this fallacy by taking due care. In the case of Ignoring a Common Cause, the key to avoiding this fallacy is to be careful to check for other factors that might be the actual cause of both the suspected cause and the suspected effect. If a person fails to check for the possibility of a common cause, then they will commit this fallacy. Thus, it is always a good idea to always ask "could there be a third factor that is actually causing both A and B?"

Click For Fallacy Description
16
False Dilemma
AKA Black & White Thinking

Category: Fallacies of Presumption

A False Dilemma is a fallacy in which a person uses the following pattern of "reasoning":

  1. Either claim X is true or claim Y is true (when X and Y could both be false).
  2. Claim Y is false.
  3. Therefore claim X is true.
This line of "reasoning" is fallacious because if both claims could be false, then it cannot be inferred that one is true because the other is false. That this is the case is made clear by the following example:
  1. Either 1+1 =4 or 1+1=12.
  2. It is not the case that 1+1 = 4.
  3. Therefore 1+1 =12.
In cases in which the two options are, in fact, the only two options, this line of reasoning is not fallacious. For example:
  1. Bill is dead or he is alive.
  2. Bill is not dead.
  3. Therefore Bill is alive.

Click For Fallacy Description
356
Appeal to Novelty
AKA Appeal to the New, Newer is Better, Novelty

Category: Fallacies of Relevance (Red Herrings) → Distracting Appeals

Appeal to Novelty is a fallacy that occurs when it is assumed that something is better or correct simply because it is new. This sort of "reasoning" has the following form:

  1. X is new.
  2. Therefore X is correct or better.
This sort of "reasoning" is fallacious because the novelty or newness of something does not automatically make it correct or better than something older. This is made quite obvious by the following example: Joe has proposed that 1+1 should now be equal to 3. When asked why people should accept this, he says that he just came up with the idea. Since it is newer than the idea that 1+1=2, it must be better.

This sort of "reasoning" is appealing for many reasons. First, "western culture" includes a very powerful commitment to the notion that new things must be better than old things. Second, the notion of progress (which seems to have come, in part, from the notion of evolution) implies that newer things will be superior to older things. Third, media advertising often sends the message that newer must be better. Because of these three factors (and others) people often accept that a new thing (idea, product, concept, etc.) must be better because it is new. Hence, Novelty is a somewhat common fallacy, especially in advertising.

It should not be assumed that old things must be better than new things (see the fallacy Appeal to Tradition) any more than it should be assumed that new things are better than old things. The age of a thing does not, in general, have any bearing on its quality or correctness (in this context).

Obviously, age does have a bearing in some contexts. For example, if a person concluded that his day old milk was better than his two‐month old milk, he would not be committing an Appeal to Novelty. This is because in such cases the newness of the thing is relevant to its quality. Thus, the fallacy is committed only when the newness is not, in and of itself, relevant to the claim.

Click For Fallacy Description
2
Appeal to the Consequences of a Belief
Argumentum Ad Consequentium

Category: Fallacies of Relevance (Red Herrings) → Distracting Appeals

The Appeal to the Consequences of a Belief is a fallacy that comes in the following patterns:

#1: X is true because if people did not accept X as being true, then there would be negative consequences.
#2: X is false because if people did not accept X as being false, then there would be negative consequences.

#3: X is true because accepting that X is true has positive consequences.
#4: X is false because accepting that X is false has positive consequences.

#5: I wish that X were true, therefore X is true. This is known as Wishful Thinking.
#6: I wish that X were false, therefore X is false. This is known as Wishful Thinking.

This line of "reasoning" is fallacious because the consequences of a belief have no bearing on whether the belief is true or false. For example, if someone were to say "If sixteen-headed purple unicorns don't exist, then I would be miserable, so they must exist", it would be clear that this would not be a good line of reasoning. It is important to note that the consequences in question are the consequences that stem from the belief. It is important to distinguish between a rational reason to believe (RRB) (evidence) and a prudential reason to believe (PRB) (motivation). A RRB is evidence that objectively and logically supports the claim. A PRB is a reason to accept the belief because of some external factor (such as fear, a threat, or a benefit or harm that may stem from the belief) that is relevant to what a person values but is not relevant to the truth or falsity of the claim. The nature of the fallacy is especially clear in the case of Wishful thinking. Obviously, merely wishing that something is true does not make it true. This fallacy differs from the Appeal to Belief fallacy in that the Appeal to Belief involves taking a claim that most people believe that X is true to be evidence for X being true.

Click For Fallacy Description
15
↑ Answer Frequency
posted by We Deserve Better     
Click These For Fallacy Descriptions ← Click these for fallacy descriptions.
Speed
Bonus
+
10
About this Pythagorean Triangle
Pythagorean Triangle with Senses, Trivium, & Quadrivium
Winning in the Light Direction
Winning in the Light Direction
Winning in the Light Direction
Occupy Your Brain
Occupy Your Brain
Pop, Logic,
& Drop It

100 
Winning Flagpole Base
LOGIC
LOGIC
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Occupy Your Brain
Session Score
Your session will time out after about
24 minutes without playing.
Beginner
0
Medium
HIGH SCORES
JAO13,013
EAB12,147
WDB10,055
RCO9,051
DEB8,541









CONGRATS!
You've made it to the
top 100 high score list!

Don't stop now, but
your initials go here: