What Is Inductive Reasoning? Learn the Definition of Inductive Reasoning With Examples, Plus 6 Types of Inductive Reasoning

Inductive reasoning, or inductive logic, is a type of reasoning that involves drawing a general conclusion from a set of specific observations. Some people think of inductive reasoning as “bottom-up” logic, because it involves widening specific premises out into broader generalizations.

Here is a basic form of inductive reasoning, with a premise based on concrete data and a generalized conclusion:

1. 1. All the swans I have seen are white. (Premise)
2. 2. Therefore all swans are white. (Conclusion)

In this example, the conclusion is actually wrong—there are also black swans. This is what’s called a “weak” argument. However, it’s easy to make the conclusion stronger, by making it more probable:

1. 1. All the swans I have seen are white. (Premise)
2. 2. Therefore most swans are probably white. (Conclusion)

Inductive reasoning is used in a number of different ways, each serving a different purpose:

1. 1. We use inductive reasoning in everyday life to build our understanding of the world.
2. 2. Inductive reasoning also underpins the scientific method: scientists gather data through observation and experiment, make hypotheses based on that data, and then test those theories further. That middle step—making hypotheses—is an inductive inference, and they wouldn’t get very far without it.
3. 3. Finally, despite the potential for weak conclusions, an inductive argument is also the main type of reasoning in academic life.

There are a few key types of inductive reasoning.

1. 1. Generalized. This is the simple example given above, with the white swans. It uses premises about a sample set to draw conclusions about a whole population.
2. 2. Statistical. This form uses statistics based on a large and random sample set, and its quantifiable nature makes the conclusions stronger. For example: “95% of the swans I’ve seen on my global travels are white, therefore 95% of the world’s swans are white.”
3. 3. Bayesian. This is a method of adapting statistical reasoning to take into account new or additional data. For instance, location data might allow a more precise estimate of the percentage of white swans.
4. 4. Analogical. This form notes that on the basis of shared properties between two groups, they are also likely to share some further property. For example: “Swans look like geese and geese lay eggs, therefore swans also lay eggs.”
5. 5. Predictive. This type of reasoning draws a conclusion about the future based on a past sample. For instance: “There have always been swans on the lake in past summers, therefore there will be swans this summer.”
6. 6. Causal inference. This type of reasoning includes a causal link between the premise and the conclusion. For instance: “There have always been swans on the lake in summer, therefore the start of summer will bring swans onto the lake.”

Inductive reasoning is one of the two main types of reasoning that people base their beliefs on. The other is deductive reasoning, or what’s sometimes known as a syllogism.

An example of deductive reasoning is:

“All birds have feathers and swans are birds. Therefore swans have feathers.”

Logicians often prefer a deductive argument, because it produces rock-solid conclusions. However, this form of thinking is only useful in some, limited circumstances. Usually, it involves the opposite of generalizing, as it starts with general principles and works progressively towards a specific conclusion. It is sometimes known as a “top-down” argument, in contrast to the “bottom-up” approach of inductive reasoning.

Instead of being weak or strong, deductive reasoning produces either a valid argument or an invalid one, based on whether the premises necessitate the conclusion.

There is a third process that is important in scientific reasoning, even though its conclusions can be unreliable. This process is abductive reasoning, which takes true premises and seeks the most likely explanation for them—like taking the best guess.
As with inductive reasoning, abductive reasoning presents an opportunity to develop theories that a person can go on to test further. For example:

“There are always swans on the lake in summer but not in winter. Therefore swans like warm water.”

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