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Inductive arguments claim that their conclusion probably follows the premises. Inductive arguments are always stronger or weaker, not true or false.
Inductive arguments often include words or phrase such as: probably, most likely, chances are that, it is reasonable to suppose that, we can expect that, it seems probably that, etc.
Deductive arguments claim that their conclusion necessarily follows from the premises if the premises are true and reasoning valid. We'll talk about those in the next chapter.
Are the following arguments inductive or deductive?
We use inductive reasoning just about every day.
What's an inductive argument that you've used lately?
What would make you happier? Which political candidate would do a better job? How to get more students to do their homework?
Familiarity with the principles of inductive logic will make you less likely to commit errors in your thinking.
There are three common types of inductive arguments: generalizations, analogies, and casual arguments.
Generalization
Drawing a conclusion about a certain characteristic of a population based on a sample of it.
When we use the word generalization in regular conversation, we often are using it in a negative way. I'm sure you've had instances when you've been told not to make a generalization about someone or something. Taking a generalization too far can lead to stereo-typing. Yet, when used properly, generalizations give us a lot of information about the world around us.
Polls and surveys are the most common examples of inductive generalization.
Polls provide a window into how people think and feel. Marketing firms and public policy makers nearly always consult some sort of polling before taking any major action or launching a new product.
To ensure that a generalization about a population is reliable, pollsters use a method known as sampling to collect data about a population that is large or diverse and where it would be too costly and time-consuming to study the whole population.
Sampling
Selecting some members of a group and making generalizations about the whole population on the basis on their characteristics.
Representative Sample
A sample that is similar to the larger population from which it was drawn.
Random Sample
Every member of the population has an equal chance of becoming part of the sample.
Self-Selected Sample
A sample where only the people most interested in the poll or survey participate (The Voice voting, elections).
The larger the sample, the more reliable the generalization can be.
Accuracy also depends on the amount of variation withing a population. The more variation there is, the larger the sample size must be to produce a more accurate result.
Question wording within a poll or survey is extremely important. Bias may result from the way a question is worded.
The National Abortion Rights Action League took a poll in 2003 and asked the following question in two different ways.
29% of respondents favored "prohibiting abortions" while 50% favored "protecting life."
Slanted Question
A question that is written to elicit a particular response.
Loaded Questions
A fallacy that assumes a particular answer to another unasked question
Questions in polls should be simple and cover only one topic to avoid this.
Also avoid creating the fallacy of false dilemma:
"State College is currently experiencing a financial crisis. Do you think our college should raise tuition or increase class size?"
Push Poll
A poll that starts by presenting the pollsters' views before asking for a response.
By presenting the pollsters' views first, the poll becomes automatically slanted, because people tend to form opinions about the questions or even uncritically accept the views of those they perceive as authority figures.
Asking the right questions
Numbers lie
1. The Premises are True
Ensure the information is accurate and coming from credible sources.
2. The Sample is Large Enough
The larger the sample, the more reliable the conclusion.
3. The Sample is Representative
A sample can be large, but still not representative.
EXAMPLE: In the 1980s, women were rarely included in clinical drug trials. Because of this poor sample, women sometimes ended up with treatments or drugs that were inappropriate for them.
4. The Sample is Current and Up-to-Date.
Outdated samples don't provide information on current issues on concerns.
5. The Conclusion is Supported by the Premise(s)
The conclusion should follow logically from the premises.
Analogy
A comparison between two or more similar events or things.
Analogies often use words or phrases such as like, as, similarly, or compared to.
Noticing similarities between things or events is one of the primary ways we learn from experience. Not every situation is identical, but we bring knowledge from similar experiences to shape our decision making and arguments.
Analogies can exist on their own as descriptive devices; you've probably talked about them in English classes before. Metaphors and similes are types of analogies.
For our purposes, though, we want to see how they are used in arguments.
Premise: X has characteristics a, b, and c.
Premise: Y has characteristics a and b
Conclusion: Therefore, Y probably also has characteristic c.
Analogies as arguments are used in virtually every area of our world. Astronomers use analogies to make predictions about the characteristics of planets similar to Earth. Lawyers look at previous court rulings on similar cases as they build their own case. Emotionally charged analogies are used in politics all the time; Winston Churchill compared the Nazi army to "a swarm of crawling locusts."
Analogies can be used to refute arguments containing weak or false analogies. One way to do this is to respond to the faulty analogy with a new one. You can also extend the analogy being used to make your own point.
Analogies can be effective tools in an argument by clarifying the key points. However, they can also be deceptively persuasive, since they appeal to our sense of imagination.
Some analogies are stronger than others. Here are a few steps for evaluating arguments based on analogies:
1. Identify What is Being Compared
2. List the Similarities
List everything you can think of and then cross out anything irrelevant.
3. List the Dissimilarities
Again, list them all and discard irrelevant ones.
The more dissimilarities there are, the weaker the argument.
4. Compare the Lists of Similarities and Dissimilarities
Are the similarities strong enough to support the conclusion? Are the dissimilarities relevant in important ways?
5. Examine Possible Counter-analogies
6. Determine If the Analogy Supports the Conclusion
Break into small groups and create an analogy for each of the following. Don't think too hard; this is meant to be fun. However, be ready to defend why your analogy fits.
Cause
an event that brings about a change or effect
Causal argument
An argument that claims something is (or is not) the cause of something else.
Premise (cause): You're eating too many French fries.
Premise (cause): You don't exercise.
Conclusion (effect): You're going to gain weight if you don't change your ways.
Correlation
When two events occur together regularly at rates higher than probability.
Positive Correlation
The incidence of one event increases the the second one increases.
Negative Correlation
The occurrence of one event increases as the other decreases.
Correlations are often the starting point in determining whether casual relationships exist. To make sure there aren't other causal factors or variables, scientists use controlled experiments.
Controlled Experiment: An experiment in which the sample is randomly divided into an experimental and a control group.
Creating effective public policy and making satisfactory life decisions both depend on being able to correctly infer causal relationships.
Why are African American students more likely to drop out of college than European American students?
Why did my last two relationships fail?
Most decisions are not clear-cut. In making a decision where there are both beneficial and harmful effects of a particular action or policy, you need to weigh the harms against the benefits. In public policy, this is called a cost-benefit analysis.
1. The Evidence for a Causal Relationship Should Be Strong
Do your research before jumping to a conclusion!
The more evidence there is, the strong the argument.
Be wary of anecdotal evidence.
2. The Argument Should Not Contain Fallacies
3. The Data Should Be Current and Up-To-Date
A conclusion may be incorrect because a particular casual relationship that was once true may no longer hold.
This is where a lot of "old wives tales" come from.
4. The Conclusion Should Not Go Beyond the Premises
Remember that inductive arguments are not out to prove things definitively. Rather, they prove that a conclusion probably follows the premises.
Evaluate the following causal arguments: