Yolanda: Gaining access to computers without authorization and manipulating the data and programs they contain is comparable to joyriding in stolen cars; both involve breaking into private property and treating it recklessly. Joyriding, however, is the more dangerous crime because it physically endangers people, whereas only intellectual property is harmed in the case of computer crimes.
Arjun: I disagree! For example, unauthorized use of medical records systems in hospitals could damage data systems on which human lives depend, and therefore computer crimes also cause physical harm to people.
Summarize Argument: Counter-Position
Arjun concludes that computer crimes also cause physical harm to people. He supports this with an example: unauthorized use of hospital medical records could damage data systems that are critical to human lives.
Identify and Describe Flaw
Arjun concludes that computer crimes do cause physical harm based on the example that unauthorized use of hospital medical records could damage data systems that are critical to human lives. In other words, in order to draw his conclusion, he must assume that something that could happen actually will happen.
A
fails to maintain a distinction made in Yolanda’s argument
Yolanda makes a distinction between joyriding and computer crimes. Arjun counters this distinction by claiming that computer crimes also cause physical harm. He doesn’t ignore her distinction.
B
denies Yolanda’s conclusion without providing evidence against it
Arjun denies Yolanda’s conclusion, but he does provide evidence: the example of unauthorized use of hospital medical records. The flaw lies in the relationship between this evidence and his conclusion.
C
relies on the actuality of a phenomenon that he has only shown to be possible
Arjun’s premise states that unauthorized use of hospital medical records could damage certain data systems, while his conclusion states that computer crimes do cause physical harm. So his conclusion depends on the actuality of something that he’s only shown to be a possibility.
D
mistakes something that leads to his conclusion for something that is necessary for his conclusion
This is the cookie-cutter flaw of mistaking sufficiency for necessity. Arjun doesn’t do this; he just gives an example to support his conclusion.
E
uses as evidence a phenomenon that is inconsistent with his own conclusion
This is the cookie-cutter flaw of internal contradiction. Arjun’s evidence may not support his conclusion well, but it is consistent with his conclusion.
Premiums for automobile accident insurance are often higher for red cars than for cars of other colors. To justify these higher charges, insurance companies claim that, overall, a greater percentage of red cars are involved in accidents than are cars of any other color. If this claim is true, then lives could undoubtedly be saved by banning red cars from the roads altogether.
Summarize Argument: Phenomenon-Hypothesis
The author hypothesizes that banning red cars could save lives, based on the claim that a greater percentage of red cars are involved in accidents than are cars of any other color.
Identify and Describe Flaw
This is a cookie-cutter “correlation does not imply causation” flaw, where the author sees a positive correlation and then assumes that one thing causes the other, without ruling out alternative hypotheses. She assumes that red cars cause car accidents simply because more red cars are involved in accidents. She goes on to conclude that banning red cars could save lives.
She overlooks the possibility that some other, underlying factor could be causing the correlation— maybe there’s something that causes people to buy red cars and to be involved in car accidents.
A
accepts without question that insurance companies have the right to charge higher premiums for higher-risk clients
Irrelevant— even if she does accept this, it has nothing to do with her argument. Her argument is about the correlation between red cars and accidents; insurance company rates are just context.
B
fails to consider whether red cars cost the same to repair as cars of other colors
Irrelevant— she may not consider repair costs, but this isn’t the flaw because repair costs don’t affect her argument. She hypothesizes that banning red cars could save lives; it doesn’t matter how much they cost to repair.
C
ignores the possibility that drivers who drive recklessly have a preference for red cars
This describes an alternative hypothesis that the author ignores. She assumes red cars cause accidents, without considering that some other, underlying factor may be causing the correlation— maybe reckless drivers just like red cars and that’s why more red cars are in accidents.
D
does not specify precisely what percentage of red cars are involved in accidents
Irrelevant— the exact percentage of red cars doesn’t matter, since we already know that “a greater percentage of red cars are involved in accidents” than cars of other colors.
E
makes an unsupported assumption that every automobile accident results in some loss of life
The author never makes this assumption. She just assumes that some car accidents result in some loss of life. Based on this assumption and the assumption that red cars cause accidents, she concludes that banning red cars could save lives.
A
Children are more likely than adults to be given meals composed of foods lacking especially distinctive flavors.
B
Children are less likely than adults to see a connection between their health and the foods they eat.
C
Children tend to have more acute taste and to become sick more often than adults do.
D
Children typically recover more slowly than adults do from sickness caused by food.
E
Children are more likely than are adults to refuse to eat unfamiliar foods.