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Deductive and Inductive Reasoning

Do you like watching detective shows? You might have seen how a detective would analyze pieces of evidence–CCTV footage of a person entering the crime scene, their fingerprints on a weapon left on the scene, and a massive debt to their name–to come up with a likely suspect for a robbery case. 

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Deductive and Inductive Reasoning

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Do you like watching detective shows? You might have seen how a detective would analyze pieces of evidence–CCTV footage of a person entering the crime scene, their fingerprints on a weapon left on the scene, and a massive debt to their name–to come up with a likely suspect for a robbery case.

This kind of investigation requires logical reasoning to reach a sound conclusion. In science, we also use logical reasoning to come up with explanations for natural phenomena. So, let's dive into the world of deductive and inductive reasoning!

  • First, we will discuss two kinds of logical reasoning: deductive and inductive.
  • Then, we will look at their definition.
  • After, we will cite examples of how they can be applied in various situations.
  • Finally, we will identify their similarities and differences.

How is logical reasoning used in developing theories?

Scientists use a combination of careful planning, creative thinking, and logical reasoning to come up with information and explanations for natural phenomena. While there are diverse elements in conducting a scientific inquiry, essential characteristics set science apart from other ways of describing and explaining natural phenomena.

Scientific methods are rigorous processes that involve making observations, defining problems, forming logical explanations in the form of hypotheses, testing these hypotheses through experiments, and drawing conclusions.

Scientists communicate these results to other members of the scientific community. When an explanation is tested and supported by other findings, it may become a theory. However, as more information is collected, theories can be revised.

Scientists use logical reasoning in carrying out scientific inquiries.

A hypothesis is a proposed explanation for a phenomenon that can be subjected to testing.

A theory is a tested and verified hypothesis.

What is the definition of deductive and inductive reasoning?

There are two types of logical reasoning: deductive and inductive. Their definition is shown below.

Deductive reasoning is a form of logical reasoning that uses a general principle to predict specific results. These predictions are valid as long as the general principles are valid.

Inductive reasoning is a form of logical reasoning that uses many specific observations to arrive at a general conclusion. A conclusion derived from induction is called an inductive inference.

Figure 1 below shows the fundamental difference in logical pattern between the two types.

What are some examples of deductive and inductive reasoning?

Let’s discuss examples of deductive and inductive reasoning.

For each type of logical reasoning, we will start with scenarios you might encounter in your everyday life. Then, we will discuss how these forms of logical reasoning are used in scientific inquiry.

Examples of Deductive Reasoning

Deductive reasoning begins with general premises from which we are to come up with a specific conclusion.

Let’s say I asked you if Bob is required to attend class today.

You might come up with a response this way:

  • All students are required to attend class today.

  • Bob is a student.

  • Therefore, Bob is required to attend class today.

One way to think about deductive reasoning is by using an “if…then” statement. In Bob’s case: If all students are required to attend class today, and Bob is a student, then Bob has to attend class today.

Here’s an example you might encounter in biology:

  • All living organisms are made up of Cells.

  • All humans are living organisms.

  • Therefore, all humans are made up of Cells.

In scientific inquiry, formulating and testing hypotheses typically involves deductive reasoning. Deductions are basically predictions of what will happen if a specific hypothesis is correct. When a hypothesis is tested, we will see whether or not the results are as expected.

For example, newly hatched ducks tend to make a bond with the first adult individual that they see. They will stay close to that adult, increasing their chances of survival. In the wild, this would typically be their mother.

We might ask: If a newly hatched duck sees an adult human first, will they make a bond with that human (Fig. 2)?

We can come up with the following deduction:

  • All newly hatched ducks make a bond with the first adult they see and stay close to the adult.

  • The first adult that a set of newly hatched ducks sees is Bob.

  • Therefore, the newly hatched ducks will stay close to Bob.

We can then formulate the hypothesis as follows:

  • If a set of newly hatched ducks see Bob, then they will stay close to Bob.

This hypothesis can be tested through a series of experiments. As strange as the hypothesis may sound, experiments have been conducted on this very topic. The results support the hypothesis: newly hatched ducklings stayed close to humans, provided that they made specific acoustic (a “kom kom kom” call) and visual (movement of the hand or the whole person away from the ducks) signals.

Scientists investigating climate change can use the same deductive reasoning to postulate that if a region’s climate becomes warmer, then the distribution of Plants and Animals in that region will change. This is based on the premise that plants and animals have traits that are adapted to their immediate environment. They can then test this hypothesis by looking at areas with rising average temperatures and checking for changes in plant and animal distribution.

As mentioned earlier in the definition, a deduction will only hold true if the premises are correct. If the premises are incorrect, so will be the deductions even if the logical reasoning is valid. For this reason, deductive reasoning is more often applied in mathematics and physics, where the premises can be expressed as equations or axioms.

Axioms are self-evident truths that do not need evidence (for example, that "the whole is greater than a part").

Consider the following:

  • All squares have four sides.

  • All triangles are squares.

  • Therefore, triangles have four sides.

We know that the conclusion in this example is not logically sound. But why? While the reasoning is valid, the argument is based on a false premise: that all triangles are squares. By definition, triangles have three sides, and squares have four sides.

Of course, false premises are harder to identify in actual research.

In our previous example, for instance, the first premise that all newly hatched ducks bond with and follow the first adult they see was, in itself, a conclusion that researchers made after observing possibly thousands of ducks, a generalization extrapolated from observed cases. In short, the researchers assumed that this behavior holds true for all ducklings.

For this reason, complex sciences typically use deductive reasoning in combination with inductive reasoning, which we will go into in the next section.

Examples of inductive reasoning

Whereas the pattern of thinking in deductive reasoning is from general to specific, inductive reasoning entails reasoning from specific observations to reach a general conclusion.

Let’s say you want to avoid heavy traffic today. You noticed that in the past three months, the traffic tends to be heavy between 8AM to 10AM. You conclude that to avoid traffic, you have to drive earlier than 8AM or later than 10AM.

With inductive reasoning, conclusions are made from careful observations and analysis of relatively large amounts of data. It is important to note that the strength of inductive reasoning comes from the quantity and quality of supporting evidence.

For example, from your interaction with ten friendly dogs in your neighborhood, you cannot strongly argue that all dogs in the world are friendly.

Inductive reasoning is commonplace in descriptive science. For example, based on a series of rigorous experiments where all observed pieces of copper conducted electricity, researchers can strongly argue that all copper conducts electricity.

The same logical reasoning can be applied to studying the human brain: a researcher might observe the brain activity of many subjects while they are exposed to a specific stimulus, say, food. They can collect this type of data from imaging equipment (Fig. 3).

Then, from raw data, they can analyze trends: for the majority of the subjects, which part of the brain lights up when exposed to pictures of food? They can then make an inference that the part of the brain that lights up when exposed to the stimulus controls the response to the stimulus.

Darwin’s theory of evolution by Natural Selection is an example of how inductive reasoning can be used to explain complex phenomena. Rather than drawing from general principles, Darwin made a generalization from many specific pieces of evidence including:

  • Comparing the anatomy of different organisms

  • Field observations in the Galapagos Islands

  • Observations of Selective Breeding

Darwin’s observations can be summarized as follows:

  • There is Genetic Variation among individuals within a population.

  • Individuals have to compete due to limited resources and other external factors.

  • Individuals with favorable traits are more likely to survive and reproduce.

  • In succeeding generations, more individuals will have these beneficial traits.

  • As these traits accumulate, the population evolves.

From these observations, Darwin theorized that the differential ability of organisms to survive and reproduce leads to change in the genetic makeup of the population over time.

Complexities of the natural world make inductive reasoning necessary because there are phenomena that are difficult to deduce from “first principles” (or “correct principles”).

Inductive inferences are not drawn from a set number of logical steps (for example, A=B, B=C, therefore A=C); instead, they involve open-ended sets such as extrapolating past to future events (the traffic will be heavy from 8AM to 10AM) or observed to unobserved cases (the behavior of observed ducks must be the behavior of all ducks).

For this reason, inductive inferences cannot be proven to be true without a doubt, but they can be supported by experimental testing and reach scientific consensus; meaning, many scientists think that the hypothesis explains the known data well and stands up to experimental testing.

Difference between inductive and deductive reasoning

Let’s summarize the differences between deductive and inductive reasoning in the following table (Table 1):

Deductive reasoning

Inductive reasoning

Thinking pattern

From general principles to specific conclusion

From specific observations to generalization or inference

Strengths or potential weaknesses

Deduction is true as long as premises or general principles hold true. If any of the premises are incorrect, so can be the deduction

Generalization requires a large amount of observation and evidence

Testing

Hypothesis (If…then statement) can be tested to be true or false

Hypothesis can be subjected to multiple tests and can reach scientific consensus

Typical application in sciences

Typically applied in mathematics and physics, where premises can be expressed as laws or equations

Typically applied in descriptive sciences such as in biology and sociology to explain complex natural phenomena

Table 1. This table highlights the differences between deductive and inductive reasoning.

Similarities between inductive and deductive reasoning

While deductive reasoning tends to be used where premises can be expressed as equations and axioms, and inductive reasoning tends to be applied in descriptive sciences, the boundaries between the two are often blurred.

Many scientific endeavors combine both approaches. Researchers subject their hypotheses to a continuous process of testing and revising, as they come closer and closer to their best estimation of laws that govern natural phenomena.

Deductive and Inductive Reasoning - Key takeaways

  • Scientists use logical reasoning in carrying out scientific inquiries.
  • Deductive reasoning uses a general principle to predict specific results.
  • A deduction is true as long as the premises are correct.
  • Inductive reasoning uses specific observations to come up with a general conclusion.
  • An inductive inference requires a substantial amount of evidence.
  • Many scientific endeavors combine both deductive and inductive reasoning.

References

  1. Zedalis, Julianne, et al. Advanced Placement Biology for AP Courses Textbook. Texas Education Agency.
  2. Reece, Jane B., et al. Campbell Biology. Eleventh ed., Pearson Higher Education, 2016.
  3. Malmquist, Sarah, and Kristina Prescott. “2.9 The Process of Science – Human Biology.” 2.9 The Process of Science – Human Biology, open.lib.umn.edu/humanbiology/chapter/2-9-the-process-of-science. Accessed 9 Sept. 2022.
  4. Nice, Margaret. Some Experiences in Imprinting Ducklings. Jan. 1953, sora.unm.edu/sites/default/files/journals/condor/v055n01/p0033-p0037.pdf.
  5. “Introduction to Logic.” Introduction to Logic, people.umass.edu/klement/100/logic.html. Accessed 9 Sept. 2022.
  6. DeGracia, Donald J., et al. “Inductive and Deductive Approaches to Acute Cell Injury - PMC.” PubMed Central (PMC), 13 Oct. 2014, www.ncbi.nlm.nih.gov/pmc/articles/PMC4897055.

Frequently Asked Questions about Deductive and Inductive Reasoning

Deductive and inductive reasoning are logical patterns of thinking. Deductive reasoning uses a general principle to predict specific results while inductive reasoning uses a number of specific observations to arrive at a general conclusion.

The main difference between deductive and inductive reasoning is their pattern of thinking: deductive reasoning starts from general principles and draws a specific conclusion, while inductive reasoning starts from specific observations to arrive at a general conclusion.

Both inductive and deductive reasoning are forms of logical reasoning used in scientific inquiry.

Inductive reasoning is important because complex phenomena are difficult to deduce from “first principles”. Unlike deductive reasoning that uses axiomatic principles, inductive reasoning relies on the quantity and quality of supporting evidence.

Scientists conducting research on climate change can use deductive reasoning to postulate that if a region’s climate becomes warmer, then the distribution of plants and animals in that region will change. This is based on the premise that plants and animals have traits that are adapted to their immediate environment. They can then test this hypothesis by looking at areas with rising average temperatures and checking for changes in plant and animal distribution.

Test your knowledge with multiple choice flashcards

_____ reasoning is a form of logical reasoning that uses a general principle to predict specific results.

____ reasoning is a form of logical reasoning that uses a number of specific observations to arrive at a general conclusion. 

What type of reasoning is shown below?  A = BB = CTherefore, A=C.

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