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By Root 755 0

” For all we know, only eight tourists were surveyed, and six of these recommend Morocco as a tourist destination. In this hypothetical case, the sample of tourists chosen was too small. Now let’s assume that the statistic “three out of every four tourists recommend Morocco” was based on a sufficiently large sample of several hundred tourists. But what if all the tourists were from Africa? Or suppose all the tourists were male or owned a travel agency specializing in trips to Africa? All of a sudden, we would have doubts as to whether these several hundred tourists were representative of tourists in general, and the statement that three of four tourists recommend Morocco would be suspect.

When evaluating situations involving representativeness, the objective is to show how a particular person, place, or thing is not representative of the larger “whole” and the argument is weakened or falls apart. On the other hand, show how a particular person, place, or thing is representative of the larger “whole” and the argument is strengthened.

Generally, the issue will not be whether a sample is large enough but whether it is diverse enough. If the sample is not drawn from relevant representative subclasses, the sample size is of little consequence. A noteworthy real-life example is the Gallup poll, as devised by George Gallup, and used notably for predicting winning candidates in national political races. In order to generalize about the opinions of the people in an entire country with respect to a given candidate or political issue, data must be gathered from subclasses based on age, education, gender, geography, professional status, race, and perhaps even religion. Other subclasses, such as body weight and hair color, would be irrelevant. Even though there may well be millions of people in a given country, the Gallup poll requires a sample size of only about 1,800 people to be statistically accurate.

Note that a representativeness assumption is different from an analogy assumption. An analogy assumption might be thought of as a side-by-side comparison of two things whereas a representativeness assumption might be thought of as a vertical comparison stating that a “smaller something” is just like the larger whole. An analogy assumes big “A” is equal to big “B,” but a representativeness assumption assumes little “a” is equal to big “A.”

“Good Evidence” Assumptions

Arguments should be based on evidence which itself is valid. It is only human nature to want to choose relevant evidence that supports our stance while ignoring relevant evidence which refutes it. If we want to continue smoking, for example, we may entertain only that evidence which suits our fancy, such as “smoking helps me relax, looks cool, and keeps my weight down.” Evidence contrary to this viewpoint, such as “smoking is dangerous to my health” or “smoking is too expensive,” is ignored. For the person who doesn’t like riding motorcycles, evidence chosen might include the ideas that motorcycles are dangerous and noisy, grease and dirt may ruin your clothes, helmets are uncomfortable, only two people can ride at once, and it is hard to ride motorcycles in rainy or snowy weather. Such a list might exclude that fact that motorcycles are fun and enjoyable to ride, more maneuverable, easy to find parking spaces for, and relatively inexpensive to purchase and maintain.

One of the hallmarks of objective thinking is that we should invite all relevant evidence to bear on an issue or decision at hand. We should not ignore or slant evidence if what we seek is the “truth.” The legal system followed by most countries throughout the world is modeled on the adversarial justice system. These systems of justice lend themselves to the slanting of information and evidence. Trial lawyers for the defense and prosecution present evidence in a way which maximizes the chances of their winning cases. It is important to note, however, that judges and juries must remain impartial if fairness is to be achieved.

Cause-and-Effect Assumptions

Does one event really cause another?