Reasoning from principle moves from a general principle to a specific conclusion

3 Drawing Conclusions from Insufficient Information: Induction

Inductive reasoning occurs when a conclusion does not follow necessarily from the available information. As such, the truth of the conclusion cannot be guaranteed. Rather, a particular outcome is inferred from data about an observed sample B′, {B′}⊂{B}, where {B} means the entire population. That is, on the basis of observations about the sample B′, a prediction is made about the entire population B. For example: Smoking increases the risk of cancer. Mr X smokes. How probable is it that Mr X will develop cancer? As this example illustrates, inferential statistics (t-tests, analyses of variance, and other derived forms) are based on this kind of inductive reasoning.

The problems associated with the use of induction in scientific reasoning have been addressed from both philosophical and the mathematical perspective. From the philosophical point of view, the question arises as to what this process—the drawing of universal conclusions about a given phenomenon on the basis of the probability in a simple—actually entails. Logicians, as we now discuss, have responded in a variety of ways (v. Mises, Reichenbach, Keynes, Jeffrey, and Carnap).

Carnap's influential approach to this issue represents a form of compromise. Carnap calculates probability on the basis of accumulated experiential data, where confirmations of one hypothesis as opposed to another are entered into the equation as weights, basically resulting in a kind of weighted mean.

Inductive Reasoning in Children

Inductive reasoning is an extremely valuable tool for young children. As we discussed in the previous section, the influence of world knowledge on children’s ability to evaluate deductive arguments is dramatic. Acquiring world knowledge requires regular use of induction, as children use examples in order to develop rules and make sense of the regularities that they observe in their environments. Unlike deductive reasoning, inductive reasoning does not guarantee a valid conclusion, but inducing rules is perhaps more important to developing a working knowledge of the world than using the process of deduction. There are numerous forms of inductive reasoning. In this section we will focus on category-based induction.

An important form of inductive reasoning occurs when we consider a variety of instances, form a category of information based on those instances, and then proceed to fit other new information into that category. For example, a child may see several rabbits hopping around inside a pen and then conclude that all rabbits hop. A more sophisticated inductive inference is possible based on more diverse instances. For example, when a child visits an airplane museum and sees numerous propeller planes, she may infer that all airplanes fly by means of propeller. There are differences between inductive inferences based on biological instances, which are termed natural kinds, and inferences based on artificial or manufactured objects, termed artificial kinds. Several investigators have focused on the age differences observed in inductive inferences among young children relative to older children. Of particular interest is the age at which children are able to make inferences about more diverse categories. This is important as diverse categories require more consideration of variable information. For example, it is possible to make simple inferences, such as inducing the rule that all rabbits hop on the basis of seeing numerous hopping rabbits. A more complex inference is when a child decides that all fish have a swim bladder on the basis of learning about several fish that have that organ.

There are several common categorization effects that are shown by adults. These include typicality effects (Rosch, 1975) in which a highly typical member of a category is seen to be a “good” example. For instance a golden retriever is a high typical dog, being medium-sized and having a common dog shape, while a pekingese is not a typical dog having a smaller body than average and a very short face (Fig. 5.16). The resulting typicality effect is that the golden retriever will be judged to be a member of the dog category a bit faster than the pekingese. The result also influences inductive reasoning, as more typical members of a category will be judged to have traits that are universal to the category (Osherson, Smith, Wilkie, Lopez, & Shafir, 1990). Similarly, Osherson et al. (1990) demonstrated similarity effects, in which animals that are highly similar to one another are seen to have more influence over the strength of inductive arguments.

The influence of typicality and similarity effects occur in children as young as 5 years old. López, Gelman, Gutheil, and Smith (1992) evaluated these effects in children. To test whether children’s inductive inferences are sensitive to the typicality of animals, the investigators showed children pictures of animals and asked them questions about whether another example animal would have that same property as those shown in the pictures. For instance the children were shown pictures of dogs and bats. The children were told that dogs have leukocytes inside and bats have ulnaries inside. Based on these statements, children are more likely to conclude that all animals have leukocytes inside, as dogs are more typical than bats. Children as young as 5 years old also show similarity effects. For instance, when the children considered that a horse has leukocytes inside, they were more likely to judge a donkey to also have leukocytes, rather than a squirrel.

Previous research indicates that children under the age of nine are less sensitive than adults to the diversity of a sample tending to generalize similarly in their inductive inferences between diverse and uniform samples. For example, López et al. (1992) asked children ranging in age from 5 to 9 years to evaluate biological properties within diverse pairs of animals (e.g., cats and buffalo have ulnaries inside) and again in relatively uniform pairs (e.g., cows and buffalo have leukocytes inside). The children were not reliably able to apply the diversity principle when asked to consider whether another animal (such as a squirrel) has leukocytes or ulnaries. Other studies indicate that diversity-based reasoning has been found to emerge around age eight and becomes more robust by ages 10–11 years (Rhodes & Liebenson, 2015).

These studies demonstrate that inductive inference is a part of a child’s development from a relatively young age, but inductive inferences become more advanced as children move toward the age of 10. Typicality effects and similarity effects emerge relatively early by age 5. This indicates that category knowledge is growing at rapid rate by this point and nuanced distinctions can be made in evaluating the likely properties of new category members based on the similarity to previously learned category members. The sensitivity of a child to the diversity of instances is more difficult to acquire and does not emerge until around age 10.

2.2.1 Inductive reasoning

In inductive reasoning, thought processes move from specific observations to more general ones, applying the theories suggested by observation of specific circumstances to broader situations. A key feature of induction is that generally it relies on accumulation of positive instances in order to verify a theory as correct. A prime example of inductive reasoning involves the tale of the coelacanth. For many years, scientists thought that a particular ancient species of fish, the coelacanth, had been extinct since the end of the Cretaceous period, some 80 million years ago. This thinking prevailed from the year 1836, when Agassiz described and named the coelacanth from the fossil record (Thomson, 1991). For the next one hundred years, it was presumed to have been extinct, as no one had ever come across a living specimen; however, a live coelacanth was caught off the coast of South Africa in 1938. Several have since been caught since off the coasts of East Africa and Indonesia, definitively proving their existence (Erdmann, Caldwell, & Moosa, 1998), and thus reasoning that the species was extinct simply because no one had ever seen one failed spectacularly. Such are the hazards of induction.

Deductive Reasoning

‘Inductive’ reasoning argues from observed data to inferred theory. However, any theory that is arrived at by this method cannot be tested by the same method. Testing needs a different method, ‘deductive’ reasoning, which proceeds in the opposite direction from inductive reasoning.

Deductive reasoning in fact assumes a theory and on that basis predicts the kinds of data that will be observed; the validity of the assumed theory may be tested by comparing these predictions with the data that actually occur – if the predictions are at variance with the data, the theory is thereby shown to be false, while if the fit between predictions and data is good, the theory is thereby supported, though not shown incontrovertibly to be true.

It is clear that, in any type of scientific enquiry, both types of reasoning are needed. There is, however, an important difference between the two: inductive methods are a matter of trial-and-error, and so are difficult, if not impossible, to describe coherently – deductive methods are a matter of drawing conclusions from premises using well-defined logical processes.

Glossematicians certainly use the two terms ‘inductive’ and ‘deductive,’ but it is important to bear in mind that their usage does not conform exactly to the more usual explanation of the terms given here.

Complexity and Pluralistic Perspective of Reality

As a result of its underlying epistemology and its inductive and abductive approaches to knowing, naturalistic research is founded on pluralism. Thus multiple realities, each with validity, are attributed to the phenomena under study.11

With inductive reasoning, principles emerge from seemingly unrelated information. One of the hallmarks of this logic structure is the capacity for identical information to be organized differently by each individual who thinks about it. The end result of induction is the development of a complex set of relationships that emerge from and thus link smaller pieces of information (not the reduction of principles to their parts, as in deductive reasoning). It is therefore possible that the same information may have different meanings depending on the lens through which the links are seen. Therefore, there may be multiple interpretations of the same experience and all of these interpretations then make up the complexity of what is being studied. Through abductive logic, the best fit can be determined from the multiple interpretations and theories that are all viable in explaining phenomena. Using abduction ensures that the information will be revisited and reanalyzed by the researcher at several points over the course of the study to determine goodness of fit between explanation and data, thereby sharpening and rendering the interpretation credible and trustworthy. Note that the rigor criteria, believable and trustable, differ from those of experimental-type design.

 Let's say you are interested in understanding the experiences of cancer treatments. You choose to follow 20 individuals with different types of tumors and courses of treatment. The findings from each person, although yielding some similarities, indicated differences based on cultural background, access to resources, and locations and types of tumors. By observing and interviewing individuals as they move through treatments, the investigator is able to reveal the wide range of experiences while shedding light on the basic elements common to all, such as managing the trepidation and pain of chemotherapy treatments, secondary symptoms of chemotherapy such as hair loss, and fear of the unknown. 

Introduction

Deductive and inductive reasoning have been considered to be core constructs in the study of reasoning. These forms date back to ancient times with clear roots in classical Greek philosophy. Aristotle emphasized the value of deductive reasoning as a source of powerful inferences in his writings. Deductive reasoning can reliably yield a valid conclusion based on the premises provided and the premises must also be valid for the approach to be successful. Inductive reasoning by contrast may yield a valid inference and is likely to move us beyond the current known information. Inductive reasoning comes with a price; however, in the form of a greater probability of an invalid inference if we inappropriately move beyond the information that we currently have. In other words, deduction is a safe bet moving us from currently known information to a relatively safe and valid conclusion, while inductive reasoning gives us a greater leap forward in terms of new knowledge, but comes with a greater opportunity for making erroneous conclusions.

Our focus in this chapter will be on the types of psychological approaches that have been applied toward understanding deduction and induction. Experimental psychology differs from philosophy, the other main discipline associated with deduction and induction. While philosophy has focused on the certainty and the validity of these types of reasoning, the psychological approach has focused primarily on gaining experimental evidence. This means that the psychology of reasoning deals with the many complexities that arise when we attempt to use the deductive approach, the inductive approach, or a combination of the two. The outcomes of experiments have also led to theories of reasoning that then face the added challenge of ensuring compatibility with neuroscience and biological evidence, which we have already reviewed in detail in Chapters 3 and 7Chapter 3Chapter 7.

A Strong Philosophical Distinction

Deductive and inductive reasoning are often compared and contrasted. Fundamentally, deductive reasoning guarantees the validity of a conclusion, provided that the premises are true. For example, if the premises state that all planets are round and that the Earth is a planet, it is valid to conclude that the Earth is round. Induction is an argument from a specific example toward a general rule. Induction is different than deduction, as inductive reasoning does not guarantee a valid conclusion, only a probable conclusion based on experience. For example, if we have heard that both the brown recluse spider and the black widow spider are poisonous, we may be tempted to conclude that all spiders are poisonous and should be avoided. This could be true or possibly false, based on our degree of experience with spiders. We will consider this distinction in greater detail in Chapter 9.

Philosophers have noted the strong distinction between deductive and inductive reasoning. Aristotle wrote about the distinction between inductive and deductive arguments, emphasizing the power of deduction primarily. Later, David Hume emphasized the shortcomings of inductive reasoning, as it was prone to errors due to the need to infer a global rule based on the experience of only a subset of instances. In purely academic terms, there is a strong distinction between deductive and inductive inferences. This can translate into very real consequences in everyday life when we may make errors due to inductive inferences, while deductive logic will always give us a valid conclusion, provided the premises are valid.

Evaluating the differences between these styles of reasoning has been a long-standing goal of psychologists, who are often most interested in the cognitive processes that occur during inductive inference and how these may differ from those engaged during deductive inference. Adding further clarity between these processes at a biological level became possible due to the emergence of functional imaging techniques, notably fMRI and EEG. These techniques have also offered an additional method to investigate the various conditions that are possible within deduction or induction, such as comparisons of deductive reasoning with realistic content compared to pure deduction based on novel and arbitrary rules.

Which of the following is the reasoning process that moves from a general principle to specific conclusion?

When you reason from principle in a speech you move from a specific principle to a general conclusion True or false?

Which of the following is the reasoning process that moves from a general principle to a specific conclusion quizlet?

What is reasoning from principal?