Inductive Reasoning

Inductive reasoning is a method of taking the features of the sample to make a broader conclusion about the population. It is based on only observation and generalization, and hence the conclusions are probable. Inductive reasoning depends on how well the sample represents the entire population, and how the conclusions from the sample are extrapolated to a larger population.

Let us learn more about inductive reasoning, the types of inductive reasoning, methods used for inductive reasoning, with the help of examples, FAQs.

Inductive reasoning is based on the premise of a set of observations, and the conclusion is all based on these observations. The truth of an inductive reasoning conclusion is a probability because it is not based on evidence. Inductive reasoning makes larger generalizations from specific observations. Inductive reasoning progresses from specific to generalization. It discerns a pattern from specific observation and aims at generalizing it with a theory statement.

Inductive reasoning conclusion may be false even if the hypothesis is true. Inductive reasoning picks the likely and most certain observation as a conclusion. An example of inductive reasoning is that, if there is an outcome of heads on the first throw, and if there is an outcome of ahead on the second throw, then there is a possibility of getting heads on each of the throws. Another good example is when a teacher arrives in the class and finds all the students present on the first day and the second day, and then the teacher infers that all the students will be present on all the days.

Inductive reasoning hypothesis may not be fully true as the conclusion sounds logical and it may not be always true. Inductive reasoning makes a causal link between the premise and hypothesis. The reliability of the conclusion made from inductive reasoning is based on the observations. The conclusion of inductive reasoning is based on maximum probability and it may not be 100%.

The different types of inductive reasoning.is based on the method of defining the sample from the population, and also on the methods of collecting the premise to arrive at a particular conclusion. The different types of inductive reasoning are as follows.

Generalization

This is based on the observation of the sample and a broader conclusion is made about the population. The premise is made from the sample and the conclusion is made for the population. The generalization form of inductive reasoning depends on the size of the sample, the size of the population, and how well the sample represents the population.

As a simple example, if three out of four students can speak in English, it is generally concluded that 75% of the population can speak English.

Statistical Generalization

The inductive reasoning makes a conclusion about the population based on the inference made from the statistical verified sample. The sample is a statistical representation of the population. The conclusion from statistical generalization is more reliable as the sample is a proper representation of the population. The statistical sample is generally random and large, and hence it gives the most reliable conclusion about the population.

Anecdotal Generalization

The anecdotal generalization makes a conclusion about a population from a sample that is non-statistical. Based on the general features of the sample, the conclusion is made about the population. The performance of the students of a school in a set of 8 football matches is taken and the students are judged to be the best football players of the town.

The inference is less reliable than a statistical generalization as it makes a hasty generalization about the population.

Prediction

This kind of inductive reasoning makes a prediction of the future based on the current or past sample. This inductive prediction collect the premise from the instances or phenomenon. Rather than giving a generalized prediction, this inductive reasoning gives a specific statement of the probability of happening of the future event, linked to the present or past instance.

These inferences are time-specific and may have the same set of sample and population in the current or past instance.

Inference Based on Past Events

From the name reference itself, it can be guessed that this kind of inductive reasoning makes a premise based on the historic instances to make a prediction of the future. The sample for this inference is time-bound and historic, and it gives a generalized prediction about the population and for the future.

Inference Based on Current Events

Inference based on current events is similar to inference based on past events. The inference based on the current events derives conclusions about the future from the current sample set of instances or phenomena. This inference is slightly better reliable than the inference based on past events. The prediction of the weather based on the sample points of the current weather is more reliable.

Statistical Syllogism

Statistical syllogism takes a generalization of a group to make a conclusion about an individual. This property A about a population is generalized, and another member of the group is concluded to have property A. The observation that 90% of the snakes are harmless is used to conclude that a particular species of snake is harmless.

Argument from Analogy

Inference based on analogy is based on the shared properties of samples of different populations and makes a conclusion about new property for the two populations. The samples of populations A and B have common properties of p, q, r. The property of s is observed in population A, and hence it is said to be a property of population B also.

This kind of argument from analogy is also referred to as case-based reasoning, and it is frequently used in science, social sciences, philosophy, law.

Causal Inference:

Here a casual connection is made about the population from the sample. The validity of the conclusions about the population is very low in Causal Inference. The premise of the relationship between two samples taken from different populations is a very casual relationship and the exact relationship between the two populations can be confirmed only after careful examination.

The two prime methods of inductive reasoning are enumeration induction and eliminative induction. Let us check the following sentences to learn more about each of the methods of inductive reasoning.

Enumerative Induction

Enumerative induction is an important sub-category of inductive reasoning which is often used in everyday life. Here the conclusions are often made, much in excess of the premise. If in a group of 100 birds, all are white-colored, then it is conveniently concluded that all the birds are of white color. Enumerative induction makes a conclusion and it is stronger based on the number of instances that support it. Further, the enumeration induction makes a weaker conclusion, and one contradicting premise foils the conclusion.

Eliminative Induction

Eliminative induction is also referred to as variative induction, and a variety of instances are considered before making a conclusion. Here we consider the variety of instances to support the conclusion, rather than the number of instances. As the variety of instances is considered, the strength of the conclusion is high. There is consistency in the conclusion made through elimination induction. Different statistical methods are employed to eliminate certain instances which are not representative of the population or are a repeat of the instance.

☛Related Topics

• Mathematical Reasoning
• Linear Programming
• Probability
• Objective Function
• Mathematical Induction

What Is Inductive Reasoning In Maths?

Inductive reasoning is a method of drawing a conclusion for the population based on the premise from the samples. The strength of the inductive reasoning is based on the samples representation of the population, and the methods of deriving the conclusion from the premise. The conclusion of an inductive reasoning is a probable conclusion and it may not always be true.

How To Improve Inductive Reasoning?

The inductive reasoning can be improved by having the right sample representation of the population, and the variety of the premise to arrive at a conclusion.

What Are The Examples Of Inductive Reasoning?

The two quick examples of inductive reasoning is: John leaves by 8am to school and he is early to school, and so John is early to school at all times. The cost of a product is $2, and it sells for $5, and makes a good profit. Hence selling this product is very profitable. Here based on a particular instance or case, it is generalized.

What Is The Difference Between Inductive Reasoning And Deductive Reasoning?

Inductive reasoning is probable and may not always be true. Deductive reasoning is logical and true. Inductive reasoning derives a conclusion for the larger population, based on the premise collected from the sample. Deductive reasoning starts with the hypothesis, examines the possibilities to attain a specific logical conclusion.