![]() We need to lay out the conditions for holding a fact to be true, and then show that those conditions are satisfied. In other words, how would you know this fact was right, and did it turn out the way you expected? There are thus two stock issues for disputes of fact. We can make a case for why meeting such conditions would imply the claim. Or it might be highly complex, involving many parts: verify that a piece of monitoring machinery is working well, correctly perform some pipette work 100 times, correctly take the output of the monitoring machine, and finally, apply a Chi-square test to check the distribution of the results. ![]() The steps might be simple: count the people in group A, count the people in group B, report if A has more members than B. What all of these questions have in common is that we can outline the criteria that would convince you to agree to an answer prior to any data being collected. Does this chemical combination in this order produce that reagent? Does the debt-to-GDP ratio predict GDP growth? Does a theorized subatomic particle exist? The typical questions of science are disputes of fact. Some examples of disputes of fact: Did we have more returning customers this month than the last? Do children who use antibiotics get sick more frequently? What is the favorite color of colorblind men? Is the F1 score of this model higher than that of the other model? A particularly complicated dispute of fact may depend on many smaller disputes of fact to make its case. Disputes of fact are often smaller components of a larger argument. Such disagreements arise when there are concrete statements that the audience is not likely to believe without an argument. It is a very flexible technique.Ī dispute of fact turns on what is true, or on what has occurred. Each list of stock issues also forms a natural list of rebuttals in the event that we want to argue against a policy or particular value judgment. We can also use any of these patterns of argument in the negative. Stock issues greatly simplify the process of making a coherent argument. If some of the stock issues are already believed by the audience, then we can safely ignore those. Once we have classified what kind of thing it is that is under dispute, there are specific subclaims we can demonstrate in order to make our case. Stock issues tell us what we need to demonstrate in order to overcome the point of contention. Once we have identified what kind of dispute we are dealing with, automatic help arrives in the form of stock issues. A point of dispute will fall into one of four categories: fact, definition, value, and policy. It has been adapted by successive generations of rhetoricians to fit modern needs. ![]() Once we identify the kind of dispute we are dealing with, the issues we need to demonstrate follow naturally.Īncient rhetoricians created a classification system for disputes. Such disputes can be classified, and the classification tells us what to do next. All but the most trivial arguments make at least one point that an audience will be rightfully skeptical of. A point of dispute is the part of an argument where the audience pushes back, the point where we actually need to make a case to win over the skeptical audience. Causal reasoning, which is a special topic, is so important that it is covered separately in Chapter 5.Ī very powerful way to organize our thoughts is by classifying each point of dispute in our argument. The last group is called special topics, which are the strategies for making arguments specific to working with data. The next group of patterns are called general topics, which give general strategies for making arguments. The first group of patterns are called categories of disputes, and provide a framework for understanding how to make a coherent argument. There are three groups of patterns we will explore. Instead, it is possible to take insights from patterns in other fields and mold them to fit our needs. There are big differences between a courtroom, a scientific dispute, a national policy debate, and the work that we do with data in a professional setting. We canât simply lift up the patterns that structure arguments in other disciplines and plop them down precisely into data science. Instead of bushwhacking our way through the forest, we have a map to lead us to well-worn trails that take us where we need to go. One of the great benefits of studying arguments is that we can draw inspiration from patterns that have been noticed and explored by others.
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