This structures how we build arguments, common strategies used, and what kind of questions to tackle when building arguments.
Observations (collected data) alone are not enough to act on. When we connect observations to how the world works, we have the opportunity to make knowledge. Arguments are what makes knowledge out of observations.
Only in mathematics is it possible to demonstrate something beyond all doubt.
How an argument works: An argument moves from statements that the audience already believes into statements they do not yet believe. It moves from prior belief to a new belief to establish knowledge in a defensible way.
There needs to be something that the audience is tentatively willing to agree to, or else there is no way forward.
Another source of prior or background knowledge is commonly known facts.
In reality not all wisdom can be fully verified, and people rarely require omnipotence in practice.
Data Analysis is often an exercise in coming up the arguments that tackle a certain need and then use data and statistical data transformations as evidence to confirm those arguments.
Arguments are built around claims. Before hearing an argument, there are some statements the audience would not endorse. After all the analyzing, mapping, modeling, graphing, and final presentation of the results, we think they should agree to these statements. These are the claims. Put another way, a claim is a statement that could be reasonably doubted but that we believe we can make a case for.
A key part of any argument is evidence. Claims do not demonstrate themselves. Evidence is the introduction of facts into an argument.
Transformations make data intelligible, allowing raw data to be incorporated into an argument. A transformation puts an interpretation on data by highlighting things that we take to be essential.
We need some justification of why this evidence should compel the audience to believe our claim. We need a reason, some logical connection, to tie the evidence to the claim. The reason that connects the evidence to the claim is called the justification.
Finally, all justifications provide some degree of certainty in their conclusions, ranging from possible, to probable, to very likely, to definite. This is known as the degree of qualification of an argument.
There are always reasons why a justification won’t hold in a particular case, even if it is sound in general. Those reasons are called the rebuttals. A rebuttal is the yes-but-what-if question that naturally arises in any but the most self-evident arguments.
Patterns of Reasoning
Categories of Arguments
When making an argument try find what category does it fall into and what points of dispute need to be made clear.
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. All but the most trivial arguments make at least one point that an audience will be rightfully skeptical of.
A point of dispute will fall into one of four categories: fact, definition, value, and policy.
Stock issues tell us what we need to demonstrate in order to overcome the point of contention.
A dispute of fact turns on what is true, or on what has occurred. Such disagreements arise when there are concrete statements that the audience is not likely to believe without an argument. 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? The typical questions of science are disputes of fact.
Stock issues for disputes of fact:
- What is a reasonable truth condition?
- Is that truth condition satisfied?
Disputes of definition occur when there is a particular way we want to label something, and we expect that that label will be contested.
Stock issues with disputes of definition:
- Does this definition make a meaningful distinction?
- How well does this definition fit with prior ideas?
- What, if any, are the reasonable alternatives, and why is this one better?
When we are concerned with judging something, the dispute is one of value. For example, is a particular metric good for a business to use? We have to select our criteria of goodness, defend them, and check that they apply.
Which is more important, customer satisfaction or customer lifetime value? We often have to justify a judgment call.
For disputes of value, the stock issues are:
- How do our goals determine which values are the most important for this argument?
- Has the value been properly applied in this situation?
Disputes of policy occur whenever we want to answer the question, “Is this the right course of action?” or “Is this the right way of doing things?” Recognizing that a dispute is a dispute of policy can greatly simplify the process of using data to convince people of the necessity of making a change in an organization. Should we be reaching out to paying members more often by email? Should the Parks Department do more tree trimming?
Stock issues of disputes of policy are:
- Is there a problem?
- Where is credit or blame due?
- Will the proposal solve it?
- Will it be better on balance?
Aka: Ill, Blame, Cure, and Cost.
Tools to build and reason about arguments.
A specific-to-general argument is one concerned with reasoning from examples in order to make a point about a larger pattern. The justification for such an argument is that specific examples are good examples of the whole. A particularly data-focused idea of a specific-to-general argument would be a statistical model. We are arguing from a small number of examples that a pattern will hold for a larger set of examples.
General-to-specific arguments occur when we use beliefs about general patterns to infer results for particular examples. While it may not be true that a pattern holds for every case, it is at least plausible enough for us to draw the tentative conclusion that the pattern should hold for a particular example. For example, it is widely believed that companies experiencing rapid revenue growth have an easy time attracting investment. So if we demonstrate that a company is experiencing rapid revenue growth, it seems plausible to infer that the company will find it easy to raise money.
The archetypal rebuttal of a general-to-specific argument is that this particular example may not have the properties of the general pattern, and may be an outlier.
Argument By Analogy
Every mathematical model is an analogy. If we have two clients with a similar purchasing history to date, it seems reasonable to infer that after one client makes a big purchase, the other client may come soon after. The justification for argument by analogy is that if the things are alike in some ways, they will be alike in a new way under discussion.
The rebuttal for argument by analogy is the same as the rebuttal for general to specific arguments that what may hold for one thing does not necessarily hold for the other. Physical objects experience second-order effects that are not accounted for in the simplified physical model taken from an engineering textbook.
Patterns of argument building that pop up in settings where we are using data professionally. But not only.
- Optimization: An argument about optimization is an argument that we have figured out the best way to do something, given certain constraints.
- Bounding Case: Sometimes an argument is not about making a case for a specific number or model, but about determining what the highest or lowest reasonable values of something might be.
- Cost/Benefit Analysis: In a cost/benefit analysis, each possible outcome from a decision or group of decisions is put in terms of a common unit, like time, money, or lives saved. The justification is that the right decision is the one that maximizes the benefit (or minimizes the cost).
- Thinking with Data, Max Shron: http://vimeo.com/98768831. And http://blog.mortardata.com/post/91270402361/max-shron-thinking-with-data-talk
(Identifying) Weak Reasoning
A fallacy is an argument that uses poor reasoning. An argument can be fallacious whether or not its conclusion is true.
Cognitive biases are tendencies to think in certain ways. Cognitive biases can lead to systematic deviations from a standard of rationality or good judgment, and are often studied in psychology and behavioral economics.
comments powered by Disqus