cakewalk.
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In order to confuse test takers, the AWA essays will always contain some
CHAPTER 6
flawed reasoning or illogical statements. In particular, some of the
paragraphs on the AWA Argument essay will contain flawed reasoning, which can appear in many forms. While these forms can potentially be unlimited in number, most of them can be categorized into 6 groups.
These are potentially the 6 types of false reasoning that you frequently see on the AWA Argument essays:
1. Creating stereotypes. Assuming that characteristics of a group in general apply to each member of that group.
2. Assuming that a certain condition is necessary for a certain outcome
3. Drawing a weak analogy between two things
4. Confusing a cause-effect relationship with a correlation
(famously known as post hoc ergo propter hoc, i.e. correlation does not imply causation)
5. Relying on inappropriate or potentially unrepresentative statistics
6. Relying on biased or tainted data (methods for collecting data must be unbiased and the poll responses must be credible)
Almost all of the argument essays contain more than one of the following flaws, so it is important that you are aware of each and every possible flawed reasoning.
1. The Stereotypical Assumption
We see this happen quite often in our everyday life. People resort to
creating stereotypes of a particular person, or a group of people. However, common sense says that it is pretty unrealistic to describe a group and
then expect that every single member fulfills the very same characteristics.
For example, saying that ‘Girls are weaker than guys’ or that ‘Asians are geniuses’ is just plain wrong, because in both the cases, the statements are generalized in nature. While both the statements can seem to be true for the most part, we all know that in the real world, some girls are stronger
than guys, and that some Asians fail their tests. Which means, one cannot simply make a sweeping statement in either of the aforementioned cases.
Now, you can easily remember this type of false reasoning on the AWA, by relating it to stereotypes. We generally think of stereotypes as harmful
because they unfairly limit a certain group to a predefined characteristic that often has little to no evidence. Hence, in order to avoid falling trap to this stereotypical assumption fallacy, you should immediately consider any sentence that generalizes a particular group as plain wrong, and attack
that assumption when you are writing your response.
2. The Necessity Assumption:
This is a very frequent type of false reasoning that hides in plain sight.
Most students simply cannot identify that this type of reasoning is wrong.
The author of an argument usually assumes that a certain condition is necessary to achieve the desired result. This sounds reasonable, but the problem here is, the author simply says that it is necessary to do
evidence which proves that there is no other means of achieving a similar result.
For example, the arguments says that, if students have to perform better in schools, it is necessary that the teachers be more active in the classroom.
Now, this looks like a perfectly logical statement to anyone. But the problem is, the author has not considered whether there are any other ways students can perform better in schools. The author makes a simple statement that outlines only one necessity – the teachers being active – and does not talk about the relevant evidences, or the possibility of other ways to achieve the same result. Of course, there are other factors
involved: maybe students can perform better if they study every day at home, or if the school redesigns the curriculum to suit the needs of students, etc. So, you should keep in mind to attack this necessity
assumption, and also to include the alternative factors or possibilities.
3. The Illogical Analogy:
Analogy is when someone comes to a conclusion about something on the basis of another thing. For example, if a giant conglomerate has doubled its sales in the last one year because it had spent over $10 million on
advertising, then in order for another giant conglomerate to double its sales, it has to spend more than $10 million on advertising.
Now, on the first glance, this might seem like a logical argument. But, if you think about it carefully, it is an illogical analogy that the author has made in order to prove his/her point. The argument may seem sound enough to agree to, but one simply can’t analogize these two scenarios, even though the size of the companies is similar.
First of all, we don’t know if the two companies are based in the same country or not. The demographics in their respective countries may respond to different incentives. And there are several other factors like industry, market size, product quality, support system, target audience, consumer trends, economic situation in the country, etc., that need to be addressed, before the author can make such a comparison. Without this
takers should hence not fall into such traps.
4. The Correlation – Causation Confusion:
As confusing and bemusing as the title is, this is one of the more frequent fallacies that appear on the AWA argument essays. More famously known as the post hoc fallacy, this is easily one of the most common types of false reasoning you’ll encounter on test day. So it is very important that you master it.
Many arguments try to confuse test takers by arguing that correlation and causation are one and the same. But in fact, they aren’t. There is actually a world of difference between them both. While correlation just means that two events have occurred simultaneously, causation means that one event is the result of another event. Now you understand how different these two are. To illustrate further, let us take this as an example: In the year 2000, Company X released their new computer called Series 5, and that same year, the US witnessed a huge economic recession. Again in 2008, the company released its second computer called Series 6, and the US had
company released its second computer called Series 6, and the US had undergone another economic recession. So, whenever this company releases a new computer, the economy goes down.
Do you see how illogical it sounds? That is the difference between
correlation and causation. The above example shows correlation, and not causation. So, one should be careful enough to understand that just
because one event happens after another, it doesn’t mean that the first event caused the other to occur.
5. The Statistical Irrelevance:
You will often find that the AWA arguments cite statistical evidence to support their claims. Now, while we appreciate any kind of statistical data that further bolsters the author’s point of view, we must also be careful to analyze the relevance of the statistical data in a particular scenario.
Sometimes, the argument may cite a statistic according to a survey where a small group of people were asked a question, and based on their views, the
author generalized the opinion of the people of the entire
city/state/country. For example, if a survey of 1000 people in New York City say that they really need a new park in the city, does it mean that the entire population of the city feel the same? In order to draw a conclusion about anything, a larger sample is required. In order to really identify the voice of the people, the survey should at least include a majority of people in the city. If the population of New York City is 10 million, then the survey should try to include the opinions of at least half that number.
Hence, test takers should keep an eye on statistics mentioned in the arguments made by the author, and try to validate the relevance or significance of the given statistical data.
6. The Data Bias:
Sometimes, even though surveys include a large number of people or a certainly large sample space, it is not enough to conclude that the results obtained from the survey are really true. Biased data is another reason why data can be manipulated with, or tainted easily. For any survey or data to
be considered legitimate it has to be collected in an unbiased, fair, and scientific manner.
For example, if a survey was conducted among children in a city, on the question “What is your favorite color?” and the children were given only two options, Blue and Red, one cannot conclude that Red is the most
favorite color for the children in the city, even though 83% of the children chose Red. The survey clearly does not ask an open ended question, and is biased towards either Red, or Blue, or both. The survey is designed,
consciously or unconsciously, to yield certain desired responses, and this definitely manipulates responses by providing narrow options.
Hence, test takers should question the statistical legitimacy of a survey, and question the author whether the survey or data obtained is scientific and unbiased or not.