Inductive Reasoning  Types, Examples, Explanation
Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you go from general information to specific conclusions.
Inductive reasoning is also called inductive logic or bottomup reasoning.
Note
Inductive reasoning is often confused with deductive reasoning. However, in deductive reasoning, you make inferences by going from general premises to specific conclusions.
Table of contents
What is inductive reasoning?
Inductive reasoning is a logical approach to making inferences, or conclusions. People often use inductive reasoning informally in everyday situations.
You may have come across inductive logic examples that come in a set of three statements. These start with one specific observation, add a general pattern, and end with a conclusion.
Stage  Example 1  Example 2 

Specific observation  Nala is an orange cat and she purrs loudly.  Baby Jack said his first word at the age of 12 months. 
Pattern recognition  Every orange cat I’ve met purrs loudly.  All observed babies say their first word at the age of 12 months. 
General conclusion  All orange cats purr loudly.  All babies say their first word at the age of 12 months. 
Inductive reasoning in research
In inductive research, you start by making observations or gathering data. Then, you take a broad view of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.
Inductive reasoning is commonly linked to qualitative research, but both quantitative and qualitative research use a mix of different types of reasoning.
Types of inductive reasoning
There are many different types of inductive reasoning that people use formally or informally, so we’ll cover just a few in this article:
 Inductive generalization
 Statistical generalization
 Causal reasoning
 Sign reasoning
 Analogical reasoning
Inductive reasoning generalizations can vary from weak to strong, depending on the number and quality of observations and arguments used.
Inductive generalization
Inductive generalizations use observations about a sample to come to a conclusion about the population it came from.
Inductive generalizations are also called induction by enumeration.
Inductive generalizations are evaluated using several criteria:
 Large sample: Your sample should be large for a solid set of observations.
 Random sampling: Probability sampling methods let you generalize your findings.
 Variety: Your observations should be externally valid.
 Counterevidence: Any observations that refute yours falsify your generalization.
Statistical generalization
Statistical generalizations use specific numbers to make statements about populations, while nonstatistical generalizations aren’t as specific.
These generalizations are a subtype of inductive generalizations, and they’re also called statistical syllogisms.
Here’s an example of a statistical generalization contrasted with a nonstatistical generalization.
Statistical  Nonstatistical  

Specific observation  73% of students from a sample in a local university prefer hybrid learning environments.  Most students from a sample in a local university prefer hybrid learning environments. 
Inductive generalization  73% of all students in the university prefer hybrid learning environments.  Most students in the university prefer hybrid learning environments. 
Causal reasoning
Causal reasoning means making causeandeffect links between different things.
A causal reasoning statement often follows a standard setup:
 You start with a premise about a correlation (two events that cooccur).
 You put forward the specific direction of causality or refute any other direction.
 You conclude with a causal statement about the relationship between two things.
Good causal inferences meet a couple of criteria:
 Direction: The direction of causality should be clear and unambiguous based on your observations.
 Strength: There’s ideally a strong relationship between the cause and the effect.
Sign reasoning
Sign reasoning involves making correlational connections between different things.
Using inductive reasoning, you infer a purely correlational relationship where nothing causes the other thing to occur. Instead, one event may act as a “sign” that another event will occur or is currently occurring.
It’s best to be careful when making correlational links between variables. Build your argument on strong evidence, and eliminate any confounding variables, or you may be on shaky ground.
Analogical reasoning
Analogical reasoning means drawing conclusions about something based on its similarities to another thing. You first link two things together and then conclude that some attribute of one thing must also hold true for the other thing.
Analogical reasoning can be literal (closely similar) or figurative (abstract), but you’ll have a much stronger case when you use a literal comparison.
Analogical reasoning is also called comparison reasoning.
Inductive vs. deductive reasoning
Inductive reasoning is a bottomup approach, while deductive reasoning is topdown.
In deductive reasoning, you make inferences by going from general premises to specific conclusions. You start with a theory, and you might develop a hypothesis that you test empirically. You collect data from many observations and use a statistical test to come to a conclusion about your hypothesis.
Inductive research is usually exploratory in nature, because your generalizations help you develop theories. In contrast, deductive research is generally confirmatory.
Sometimes, both inductive and deductive approaches are combined within a single research study.
Other interesting articles
If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.
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Frequently asked questions about inductive reasoning
 What is inductive reasoning?

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.
Inductive reasoning is also called inductive logic or bottomup reasoning.
 How is inductive reasoning used in research?

In inductive research, you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.
 What’s the difference between inductive and deductive reasoning?

Inductive reasoning is a bottomup approach, while deductive reasoning is topdown.
Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.
 What are some types of inductive reasoning?

There are many different types of inductive reasoning that people use formally or informally.
Here are a few common types:
 Inductive generalization: You use observations about a sample to come to a conclusion about the population it came from.
 Statistical generalization: You use specific numbers about samples to make statements about populations.
 Causal reasoning: You make causeandeffect links between different things.
 Sign reasoning: You make a conclusion about a correlational relationship between different things.
 Analogical reasoning: You make a conclusion about something based on its similarities to something else.
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