What Is Inductive Reasoning? | Definition & Examples

Inductive reasoning involves making broad generalizations based on specific observations.

Induction is used in various academic and professional settings, as well as informal everyday conversations and tasks. This type of reasoning is especially relevant to problems involving pattern recognition, prediction, and decision-making.

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What is inductive reasoning?

Inductive reasoning involves deriving general principles or conclusions from particular examples.

Induction allows us to infer patterns or trends based on specific data and is commonly used in scientific research, data analysis, and everyday decision-making.

It belongs to the broader category of ampliative reasoning, which also includes analogical reasoning (sometimes considered a subcategory of induction) and abductive reasoning.

Whereas in deductive reasoning, conclusions don’t include novel information or hypotheses that aren’t mentioned in the premises, ampliative forms of reasoning yield conclusions that are broader in scope than the premises:

• Inductive reasoning: Derives general principles from specific observations or patterns (e.g., observing repeated sunrises and concluding that the sun always rises from the east)
• Analogical reasoning: Notes a specific similarity between two entities and infers that additional, related similarities exist (e.g., conceptualizing how the human heart works by comparing it to a water pump)
• Abductive reasoning: Explains the most likely cause of a specific observation (e.g., finding a broken window and inferring that it was caused by an attempted burglary)

Inductive vs deductive reasoning

Inductive reasoning is often defined as the opposite of deductive reasoning. Both ways of thinking are crucial, but they serve different roles:

• Inductive reasoning proposes broad principles from specific observations.
  • Example: “Researchers have observed a high recovery rate in patients who received a certain malaria medication. Therefore, there is strong evidence to suggest that this medication effectively treats malaria.”
• Deductive reasoning derives specific conclusions from broad principles.
  • Example: “By definition, all birds have feathers. Robins are birds. Therefore, robins have feathers.”

Multiple forms of reasoning often intersect in research, with inductive reasoning and deductive reasoning playing different roles:

• Inductive reasoning is used to form a hypothesis based on observed data.
• Deductive reasoning is used to make specific predictions based on a rule that is assumed to be true.

Inductive reasoning examples

Examples of inductive reasoning can be found in scientific research, market analysis, public health studies, environmental conservation efforts, and technological innovation, among other fields.

Inductive arguments

Inductive arguments are always considered informal arguments, and they play a prominent role in everyday life. An inductive argument cannot definitively prove its conclusion, but it can provide a strong case for a hypothesis. Inductive arguments are described as either strong or weak.

They are defined in contrast to deductive arguments (or formal arguments), which are described as either valid or invalid depending on whether they follow one of numerous prescribed forms.

In a strong inductive argument, the premises, if true, make the conclusion highly probable. For example, observing that every ball that is dropped falls to the ground would support a strong generalization about the existence of gravity.

In contrast, a weak inductive argument has premises that, even if true, do not make the conclusion probable. For example, observing that a small, localized group of swans are white would be a weak basis for an argument that all swans everywhere are white.

Types of inductive reasoning

Inductive reasoning can take a variety of approaches to drawing inferences:

• Analogical reasoning: Inferring further similarities between two or more situations or entities based on known shared characteristics (sometimes considered a separate form of reasoning from induction)
• Causal inference: Establishing cause-and-effect principles based on observations
• Predictive reasoning: Using past patterns or trends to forecast future events or outcomes
• Statistical inference: Making predictions or conclusions about a population based on statistical data

Inductive reasoning errors

Faulty inductive reasoning typically has one or more of the following problems:

• Inadequate samples: Drawing conclusions from limited or unrepresentative data
• Focus on irrelevant information: Diverting attention from factors directly related to the conclusion
• Overgeneralization: Ignoring contradictions or variations in observed phenomena and forming conclusions that fail to consider all relevant contexts
• Unwarranted assumptions: Treating debatable claims as true without providing evidence

In the context of argumentation, errors in inductive reasoning sometimes fall into one of many common patterns of reasoning errors known as logical fallacies.

Inductive logical fallacies

Inductive arguments that include reasoning errors are classified as informal logical fallacies if they fall into one of many identified patterns.

Inductive errors never fall into the other main category, formal logical fallacies, which involve breaking rules of formal logic that apply only to deductive arguments.

Informal logical fallacies are errors of content or context that make an argument weak and unpersuasive. Many belong to one or more of the following subcategories:

• Fallacies of relevance (or red herring fallacies) involve persuading an audience by distracting them from the main point of an argument.
  • Examples include the ad hominem fallacy, the straw man fallacy, and the appeal to authority fallacy, among others.
• Fallacies of ambiguity are characterized by the ambiguous, vague, or otherwise confusing use of language.
  • Examples include the equivocation fallacy, the motte and bailey fallacy, and the amphiboly fallacy.
• Fallacies of presumption occur when an argument is based on an unwarranted assumption or takes for granted something that has not been proven.
  • Examples include the circular reasoning fallacy, the false dilemma fallacy, and the begging the question fallacy.

Frequently asked questions about inductive reasoning

Why is deductive reasoning stronger than inductive reasoning?

Deductive reasoning is considered stronger than inductive reasoning in a specific sense:

If a deductive argument’s premises are factually correct, and its structure is valid, then its conclusion is guaranteed to be true.

An inductive argument, in contrast, can only suggest the strong likelihood of its conclusion

What is the difference between inductive and deductive reasoning?

Inductive reasoning and deductive reasoning are the two most prominent approaches to critical thinking and argumentation. Each plays a crucial role in reasoning and argumentation, but they serve different functions:

• Inductive reasoning relies on specific observations to form general conclusions. Example: “The sun has risen every day of my life; therefore, the sun will always rise every day.”
  • Cannot prove a conclusion with absolute certainty
  • Can result in informal logical fallacies (i.e., errors of content)
• Deductive reasoning (or formal reasoning) relies on general principles to form specific conclusions. Example: “All humans are mortal. Socrates was human. Therefore, Socrates was mortal.
  • Can prove a conclusion with absolute certainty if the premises are true and the argument has a valid form
  • Can result in formal logical fallacies (i.e., errors of form)

Is analogical reasoning a form of inductive reasoning?

Analogical reasoning is sometimes considered a subcategory of inductive reasoning because it involves generalizing from specific instances to derive broader principles or patterns. However, some argue that analogical reasoning is distinct from induction because it involves drawing conclusions based on similarities between cases rather than generalizing from specific instances.

Along with abductive reasoning, they are forms of ampliative reasoning (in contrast to deductive reasoning).