Exploratory Essay

As we near the end of learning about practical logic, we have three last things to take into consideration. In chapter twelve, we learned about analogies. In chapter eleven, we learned about statistics. In chapter fourteen, we learned about sophistry and pseudoscience. All three of these types of argumentation either conceal, distort, or stretch the truth in some way. They also are all inductive.
In argumentation, analogies, statistics, sophistry, and pseudoscience may be used in order to support a claim. It is important to consider the cogency of each claim in order to accept or dismiss the argument. Many of the aforementioned might attempt to persuade us with fallacious claims. Depending on the extent of the claim, some research might be

For example, we might note the similarity in two students discussing their success in doing well in school while working a part time job in that if the first student is successful in doing both, the second student should be as well. However, most analogies are not good ones, as we have to ask: will both students be taking the same classes? Will they be working the same amount of hours? It is impossible to predict the result of this situation by using this analogy unless each variable were the same for both students. It is also important to understand that analogies by themselves do not prove anything. Analogies can also be argumentative or non-argumentative. An argumentative analogy might follow a sequence or raise a question in order to support a claim. Two examples of an argumentative analogy would be saying, “It’s so hot today, I could fry an egg on the sidewalk,” or asking, “I have passed my three previous exams, why wouldn’t I pass my final exam?” A non-argumentative argument follows an example. Like so, new pilots are trained with a flight simulator. In order for an analogy to be cogent in an argument, it must be relevant and provide necessary and sufficient data. An analogy is stronger if it has more similarities than dissimilarities. It is important to note than an analogy is faulty when the dissimilarities are

Statistical arguments are also inductive and can be used to reveal or conceal facts. Like any argument, statistical arguments must contain reasonable, relevant, and necessary and sufficient data to support a claim. More frequently, we see the use of statistical arguments in studies, surveys, and polls. While polls are very expensive, if they are conducted correctly, they can be very accurate. Likewise, the larger the sample size is, the more the margin of error decreases. Of course, any bias can result in a fallacious statistic. For example, if a company who is polling people who recently bought their product, it is biased because they aren’t asking the opinion of people who haven’t bought it. More so, if the pollster is only contacting people via email, it is biased because not everyone has an email address or internet access and it is excluding them from the poll. Also, the respondent might present their own bias by falsely promoting a product because they want to tell the pollster “what they want to hear” in order to avoid offending them or being criticized. The fallacy of equivocation carries more than one meaning, either verbally or visually. One misleading term is the word “average” because it can carry three different meanings. On the other hand, visual aspects can be distorted, too. For example, after looking at a line on a graph, we might not see the fact that it’s been cropped in order to emit