Legal reasoning as a concept is a process of thinking which helps a researcher to come to decision relating to law. Law is a tool of social control that attempts to resolve conflicts in the society, to direct current activity while maintaining continuity with the past, and to control the future by laying down procedures, approaches and theories. Every decision must be guided and followed by a logical reasoning which takes into account the past decisions and statutes, the present position of the parties to the cases, and its own impact on future activity. In this article, we shall discuss inductive reasoning used in logic.

Logic is the science of reasoning. Logic as a discipline trains a person in certain methods, devices, tools and techniques that help in differentiating right reasoning from wrong ones. Reason/Reasoning is used to form inferences; conclusions drawn from propositions or assumptions that are supposed to be true. A piece of reasoning involves argument that is a relational arrangement of premises (evidences, facts, assumptions) and conclusion. There are two forms of reasoning- inductive and deductive and corresponding two types of logic. Inductive logic deals with inductive arguments and deductive logic deals with deductive arguments.

Deductive reasoning is the process in which conclusions are drawn with logical certainty from given premises. This type of reasoning is used in mathematical proofs or when dealing with formal systems.

In inductive reasoning one draws the “best” conclusion suggested by the set of observations/ experiential statements. Here observations used as evidence are always incomplete and insufficient to support a definitive conclusion, therefore one can never be certain of the conclusions one makes. Inductive reasoning is used in law to make decisions based on past legal cases. Lawyers and judges use past legal cases as analogies to make decisions about current cases. This process is known as legal reasoning by analogy and is a key part of common law legal systems.

Inductive Reasoning:

Francis Bacon introduced the concept of induction. A process of reasoning (arguing) which infers a general conclusion based on individual cases, examples, specific bits of evidence, and other specific types of premises. Induction is the most often used method of scientific research. Induction is the process of taking data, a number of instances from experience, appeals to signs, evidence or authority and causal relationship, classifying them into categories and then determining logically from them one or more generally applicable rule/s. Thus, induction is a method of logical reasoning that goes from specific set of premises based mainly on experience or experimental evidence to a general conclusion. Inductive arguments assert that the conclusion is arrived at not necessarily, but probably from the truth of the premises.

Example 1:

In Delhi last month, a nine-year-old boy died of an asthma attack while waiting for emergency aid. After their ambulance was pelted by rocks in an earlier incident, city paramedics wouldn’t risk entering the Chandni Chowk area (where the boy lived) without a police escort. Thus, based on this example, one could inductively reason that the nine- year-old boy died as a result of having to wait for emergency treatment.

Example 2:

P1: Professor x is a writer and he is wealthy.

P2: Professor y is a writer and he is wealthy.

P3: ………………………………..

P : multiple observations of the same kind

Pn: …………………………………..

C: Therefore, All professors who are writers are wealthy

No matter how many observations we have, as stated in premises P1, P2…Pn, they cannot prove the conclusion ‘All professors who are writers are wealthy.’ The premises offer, at best, reasonable grounds to ‘believe’ in such a conclusion. However, ‘belief’ is not the same as proof. The statement ‘All professors who are writers are wealthy’ not only includes observed cases of Professors who are writers and wealthy, but also includes unobserved cases (of past and future). It is possible that in future one may come across a professor which is not wealthy. Since one cannot rule out this future possibility and the leap is taken in the conclusion on the basis of limited observations of the past and present, the conclusion is only probable not certain.

The inductive method is also known as historical, or expirical or a posteriori method. It may be described as practical approach to the research problems. It tries to remove the gulf between theory and practice. This method examines various causes one after another and tries to establish causal relations between them. General principles are laid down after examining a large number of special instances or facts. The method is said to be ‘empirical’ because the formulation of principle is made only after an extensive compilation of the raw data of experience. The data may be historical or statistical data,. The historical instances are qualitative while the statistical data are quantitative. Generalizations are made after the analysis of data. Inductive reasoning starts from observable facts from which a generalization is inferred.

Guidelines for Logical and Valid Induction:

• When a body of evidence is being evaluated, the conclusion about that evidence that is the simplest but still covers all the facts is the best conclusion.
• The evidence needs to be well-known and understood.
• The evidence needs to be sufficient. When generalizing from a sample to an entire population, make sure the sample is large enough to show a real pattern. 4.
• The evidence needs to be representative. It should be typical of the entire population being generalized.

Assessment of Inductive Logic:

Arrangement 1: True Premises, Probably True Conclusion, Strong Argument

Example:

Every year in last decade there were always heavy rainfall during monsoon in Assam. Therefore, probably there will be heavy rainfall in the monsoon of this year in Assam.

In this argument the premise is actually true, as there was heavy rainfall every year in last decade during monsoon in Assam. The uniformity of nature dictates that in the next monsoon also there will be heavy rainfall in Assam. This is also what we naturally expect to happen. Thus the conclusion is probably true, and the argument is strong.

Arrangement 2: True Premises, Probably True Conclusion, Weak Argument

Example:

In 1917, 1996 and 2019, there were heavy rain fall during monsoon in Rajasthan. Therefore, probably there will be heavy rainfall during next monsoon in Rajasthan.

In this argument, the premise is actually true, the conclusion is probably true. Here we can’t apply the law of uniformity of nature as we have supporting information about only three years in a range of more than 100 years. The argument, thus, is a weak one.

Arrangement 3: False Premises, Probably True Conclusion, Strong Argument

Example:

Every Indian Prime Ministers was a poet. Therefore, it is probable that the next Indian prime minister will also be a poet.

Here the premise is actually false (some of our prime ministers were good poets, but not everyone was a poet). But if we assume that the premise is true, then we would naturally expect that the prime minister will be a poet. So, the argument is strong.

Arrangement 4: False Premises, Probably True Conclusion, Weak Argument

Example:

Some Indian Prime Ministers were logicians. Therefore, it is probable that the next Indian prime minister will be a poet.

Here the conclusion is probably true, but the premise is clearly false and even if we assume the premise as true, we cannot find any link between it and the conclusion. Therefore, the argument is weak

Arrangement 5: False Premises, Probably False Conclusion, Strong Argument

Example:

Every Rhino discovered in Assam are two-horned. Therefore, probably the next Rhino discovered in Assam will be two-horned.

In this argument the premise is clearly false, the conclusion has a high probability of being false. But if we assume the premise as true, then we naturally expect that the conclusion would have been also true. Therefore, the argument is strong.

Arrangement 6: False Premises, Probably False Conclusion, Weak Argument

Example:

Few Rhinos discovered in Assam are two-horned. Therefore, probably the next Rhino discovered in Assam will be two-horned.

In this argument, like the previous one, the premise is clearly false, the conclusion has a high probability of being false. But even if we assume the premise as true, the premise does not strongly support the conclusion as the considered instances are very few, so there is very low probability of the conclusion being true, in other words, it has a probability of being false. Therefore, the argument is weak.

Arrangement 7: True Premises, Probably False Conclusion, Weak Argument

Example:

A few Indian Space Missions have failed. Therefore, probably the next Indian Space Mission will fail.

Here the premise is actually true. But the conclusion has a high degree of being false because relevant instances are very low leading to very low degree of probability. Therefore, the argument is weak.

Arrangement 8: True Premises, Probably False Conclusion, Strong Argument

Example: Nil

If an inductive argument is strong, and if its premises are true, then the probability of the conclusion being true will be high. Therefore, Arrangement 8 is not possible.

The discussions on these possible arrangements make it clear that the relationship between truth/falsity and strong/weak inductive argument is a complex one. In most of the cases, truth of the premise or strength of the argument do not correlate. That is because the strength of argument is not dependent on the actual truth of the premises, but on the probabilistic support given by the premises to the conclusion. All that the arrangement of truth and falsity establishes is that if the premises are true and conclusion is probably false, then the inductive argument is weak. In other words, if the argument is strong and the premises true, the conclusion is probably true.

Table

The above table can be drawn parallel to the table drawn for deductive argument. However, there is one major difference. In case of deductive argument, the truth of conclusion is absolute, i.e., it can be either true or false. But in case of induction the truth of conclusion is a matter of degree. In a strong argument it has a probability of being true, in weak argument, it has higher probability of being false. The degree of probability depends on how the premises are supporting the conclusion.

Types of Inductive Reasoning:

Inductive Generalization:

Generalization is the most common type of inductive reasoning. It involves making conclusions about a larger population based on observations made on a smaller sample of that population. Inductive generalizations are also called induction by enumeration.

Example: For the past three years, the company has exceeded its revenue goal in Q3. Based on this information, the company is likely to exceed its revenue goal in Q3 this year.

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 Induction:

Statistical reasoning involves making predictions or drawing conclusions about a particular population based on statistical analysis of data collected from a sample of that population. While this type of reasoning provides context an assumption, it’s important to remain open to new evidence that might alter your theory.

Example: 90% of the sales team met their quota last month. Pat is on the sales team. Pat likely met his sales quota last month.

Casual Reasoning:

Causal inference involves making predictions or drawing conclusions about the causes of a particular phenomenon based on observed correlations or relationships between different variables.  This type of thinking involves making a logical connection between a cause and a likely effect. For the casual reasoning to be effective, it’s helpful for it to involve a strong relationship between the starting situation and the resulting inference. Observable evidence is also crucial for this type of reasoning.

Example: Joe consistently gets a stomach-ache after eating pears. He doesn’t get a stomach-ache consistently after eating any other type of fruit. Eating the pears might cause Joe’s stomach-ache.

Induction by Confirmation:

Induction by confirmation allows you to reach a conclusion by accepting specific assumptions. Police officers and detectives might use this type of reasoning to develop a theory for investigations. They may then work to collect evidence to support their theory.

Example: Anybody who breaks into a building may have opportunity, motive and means. Renee was in the area, dislikes the homeowner and has lock picks in his bag. Renee likely broke into the building.

Predictive Reasoning

Predictive reasoning involves making predictions about future events or outcomes based on patterns or trends observed in past events or outcomes. Predictive reasoning can be a useful tool for making informed decisions and planning for the future. However, it is important to recognize that past performance does not always predict future outcomes, and other factors may come into play.

Example: a stock analyst may use predictive reasoning to make predictions about the future performance of a particular stock based on past trends in the stock market.

Steps in Inductive Reasoning:

The steps in inductive reasoning can be summarized as follows:

1. Observation: The first step in inductive reasoning is to make observations of a particular phenomenon or group of instances. These observations can be made through various methods, such as experiments, surveys, or data analysis.
2. Pattern Recognition: Once observations are made, the next step is to identify patterns or regularities in the data. This involves looking for similarities or commonalities among the instances or data points.
3. Hypothesis Formulation: Based on the patterns or regularities identified, a hypothesis or tentative explanation is formulated. This hypothesis should account for the observations made and provide a possible explanation for the patterns identified.
4. Testing: The next step is to test the hypothesis through further observations or experiments. This involves collecting additional data and comparing it to the hypothesis to determine if it holds up or if it needs to be modified.
5. Evaluation: Once testing is completed, the results are evaluated to determine the strength of the hypothesis. If the results support the hypothesis, it may be considered a valid explanation for the observed phenomenon. However, if the results do not support the hypothesis, it may need to be revised or discarded altogether.
6. Conclusion: The final step is to draw a conclusion based on the evaluation of the hypothesis. If the hypothesis is supported by the data, it may be used to make predictions or generalizations about the larger population or phenomenon. However, if the hypothesis is not supported, further research may be needed to better understand the phenomenon.

Merits and demerits Inductive Reasoning:

Merits of Inductive Reasoning:

• Flexible: Inductive research is a flexible strategy that allows researchers to modify their research topics and techniques based on the data they gather. Researchers can study new concepts and phenomena that they may not have previously thought of because of this freedom.
• More Realistic: Inductive method is more realistic as it is based on facts. It deals with subject as a whole because it does not divide data into different parts.
• Future Enquiries: Inductive method helps in future enquiries. When general principles are made this method is used for investigation.
• Dynamic Approach: This method takes into consideration the changeable nature of assumptions in its analysis. It does not consider facts to be stable. It is a dynamic method. The changing data is analyzed and conclusions are drawn. The remedy is suggested to new problems.
• Develops Critical Thinking:  Inductive teaching requires students to think critically and logically, and to make inferences based on their observations and data analysis.
• Facilitates Active Learning: The inductive approach to education encourages students to investigate, evaluate, as well as synthesize material before drawing conclusions.
• Possibility of Verification: The method is more useful because its propositions can be tested and verified easily.
• Proper Attention to Complexities: This method lakes full note of the complex relationship found in actual life and examines them carefully.

Demerits of Inductive Method

• Difficult Method: This method cannot be used by a beginner or a common man because it is impossible for an ordinary person to collect facts, study them and derive some conclusions out of them. The cost is too much for him.
• Misleading: Inductive method depends upon statistical data. The application of assumptions is essential for better results. In the absence of assumptions results are misleading. The generalization obtained from a few observations is not the complete study of the topic. To fix the topic in the mind of the learner a lot of supplementary work and practice is needed.
• Danger of Bias: The propositions obtained through this method are based upon data collected by investigators. Therefore, there is a danger of investigator’s bias entering into propositions.
• Not Certain: Statistical information can provide approximate results. The principles framed under inductive method are probable but not certain.
• Time Consuming and Costly: This method needs a lot of time and energy and thus it is time consuming and laborious method.
• Limited Scope of Verification: Since the propositions obtained through this method are based on a few facts, the universal applicability of these propositions is always in doubt.
• Limited Use in Socio-Legal Studies: All the topics of science cannot be dealt with this method properly. This method is commonly used for lifeless objects of the physical science. In socio-legal studies, we study a man’s problems. As such, die method has limited use.

Conclusion:

Inductive reasoning is a type of reasoning that involves deriving generalizations or conclusions from specific observations or instances. In other words, inductive reasoning involves making inferences about the properties or characteristics of a group or category based on observations or data collected from a smaller subset of that group or category. Inductive reasoning is widely used in research fields to develop theories and models based on observed data. Researchers use inductive reasoning to analyze data, identify patterns and trends, and generate hypotheses about the relationships between variables. This process is used in many fields of research, including social sciences, education, and environmental science.