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In the field of science, law, and many more, there is no proof, only conclusions drawn from observations and evidence. A lawyer cannot prove whether an incident happened or not, but they can provide evidence.
We can consider a hypothesis true when the available evidence seems to provide verification. The conclusion is stronger when we have more evidence.
This means that the approach of reasoning helps us reach the truth. Inductive and deductive reasoning are based on evidence.
Inductive reasoning and deductive reasoning are both different approaches to research. While the former approach focuses on developing a theory, the latter tests an existing theory.
Even though both of these approaches differ in their application, they are often used as a combined approach in one large study.
In this article, we’ll gauge the difference between inductive vs. deductive reasoning and compare their strengths & limitations.
Let’s get started.
Logic can be branched out into two broad methods of reasoning, namely inductive and deductive reasoning.
Inductive reasoning is a logical thinking process in which specific observations that are believed to be true are combined to draw a conclusion to create broader generalizations and theories.
This approach helps you make large generalizations from specific observations. Inductive reasoning uncovers patterns from specific observations and progresses to generalize them with a theory.
In inductive research, if the argument is strong, the truth of the premise would indicate that the conclusion is correct. However, if the argument is weak, it means that the logic connecting the premise and the conclusion is unlikely or incorrect.
Deductive reasoning, on the other hand, works in the opposite direction of inductive reasoning. It is a logical thinking process that uses a top-down approach to go from the more general to the more specific. It involves the use of general assumptions and logical premises to arrive at a logical conclusion.
This approach involves approaching a conclusion by joining two pieces of information. The researcher processes two or more premises and confirms one premise with another premise to arrive at a logical conclusion.
In deductive research, the probability of the final statement being true is very high because this approach is based on simple rules and logic. It follows that if A=B and B=C, then A=C.
To understand inductive vs. deductive reasoning better, let’s go over the key differences.
In deductive reasoning, the process begins with a general statement to be proved with a logical conclusion.
Deductive reasoning is often called a “top-down approach” because you start with a theory, narrow it down to a hypothesis, observe the hypothesis, and ultimately reach a logical affirmation. The sphere of law heavily relies on inductive and deductive reasoning to build strong cases. For example, a legal team for truck accidents often combines specific accident observations with broader legislation to devise effective strategies. This integration aids in both constructing compelling arguments and challenging opposing claims, showcasing the importance of structured reasoning.
Deductive reasoning, on the other hand, uses quantitative analysis methods.
In deductive reasoning, if the premises are true, the conclusion has to be true.
Deductive reasoning is often used in confirmatory studies. It helps researchers test a theory or hypothesis to either prove or disprove it.
Observation: Pet dogs in my neighborhood are friendly.
Observe a pattern: All observed dogs are friendly.
Theory: All dogs are friendly.
Theory: All dogs are friendly.
Hypothesis: All pet dogs in my neighborhood are friendly.
Test hypothesis: Observe all dogs in the neighborhood.
Conclusion: 7 out of 23 dogs in the neighborhood were not friendly= Reject the hypothesis
When it comes to how we use inductive and deductive reasoning, the easy way to remember is that inductive reasoning is fast and easy to use, so we use it daily in our life. However, deductive reasoning is difficult to use in daily life since we need facts to prove the argument.
We use inductive reasoning for everyday use, such as:
Deductive reasoning is often used to solve a problem or make decisions.
Inductive reasoning is a logical thinking process that integrates observations with experiential information to draw a conclusion. You are employing the use of inductive reasoning every time you look at a set of data and then form general conclusions on knowledge from past experiences.
Inductive research is usually used when there is a lack of existing literature on a topic. This is because there is no existing theory that can be tested on the concept. The inductive training approach can be categorized into the following three stages:
To understand this approach better, let’s take a look at the following example:
Observation | Observe a Pattern | Develop a Theory |
The low-cost editing apps, App A and B, both experience app glitches. | All observed low-cost editing apps experience app glitches. | All low-cost editing apps experience app glitches. |
Let’s evaluate inductive reasoning by taking a look at its strengths and weaknesses:
Range of probabilities
One of the most prominent advantages of inductive reasoning is that it allows you to work with a range of probabilities, expanding your perception and knowledge base despite the lack of literature available.
Encourages exploration
Inductive training begins with an observation and then moves on to exploration to test the judgment made.
Limited scope
A drawback of inductive reasoning is that inferences are made from specific situations that may not have significance in the real world.
When employing deductive reasoning in research, you begin with a theory. This theory is then narrowed down into more specific hypotheses that can be tested. These are further narrowed down into observations that allow us to test the hypothesis to confirm whether the data supports or rejects the hypothesis.
The deductive training approach can therefore be categorized into the following four stages:
To understand this approach better, let’s take a look at the following example:
Start with an Existing Theory | Formulate a Hypothesis | Test the Hypothesis | Analyze the Results |
All low-cost editing apps experience app glitches. | If users select a low-cost editing app, then they will experience app glitches. | Collect data on low-cost editing apps. | 20 out of 50 of the low-cost editing apps do not experience app glitches. = Reject Hypothesis |
Let’s evaluate deductive reasoning by taking a look at its strengths and weaknesses:
Helps substantiate decisions
Deductive reasoning can be used to effectively substantiate decisions such as those related to work. Also, if the decision doesn’t produce the desired results, you can still explain why you made the decision by providing logical and objective explanations.
Reliable when the original premise is true
Another advantage of deductive reasoning is that your conclusion is almost guaranteed to be true if all the original premises are true in all situations and if the reasoning applied is correct.
Relies on initial premises being correct
Deductive reasoning heavily relies on the initial premises being correct. The final argument is invalid if even one premise is found to be incorrect.
The following types of inductive reasoning depend on two factors:
In this type of inductive research, the researcher draws a conclusion from a generalization. This means that the premise is made from the research sample, and the conclusion is drawn from the population.
For example, if three out of four students can play football, then all the students can probably play football.
In this type, the conclusion is made based on the statistically verified sample. This means that the research sample statistically represent the target population.
This is considered more reliable as the sample is selected randomly and is large.
Here, a researcher draws a conclusion based on the general features of the sample group.
This kind of inductive research makes a prediction based on the sample (current or past). In this reasoning, the researcher collects the premise from the phenomenon and draws a general prediction of the probability of happening of the future event.
Inductive reasoning based on analogy means that you draw a conclusion about new properties of two populations from the shared attributes of the samples of the two populations.
For example, say samples of populations 1 and 2 have common characteristics of u,v, and w. The property x observed in population 1 can also be said to be the property of population 2.
Here, the conclusion is drawn based on the causal connection between the samples of different populations. The validity of this conclusion is very low as it can only be confirmed by examination.
Now let’s move on to the types of deductive reasoning better to understand the process of inductive vs. deductive research.
There are three types of deductive reasoning based on two factors:
It is a commonly used type of reasoning in deductive research. It includes a set of premises followed by a conclusion.
In this type of deductive reasoning, the second premise generally affirms the first part of the first premise.
For example,
1st premise: If it’s raining today, I will wear my raincoat.
2nd premise: it is raining today.
Conclusion: I will wear my raincoat.
This kind of deductive reasoning is also known as the law of contrapositive. It is the opposite of modus ponens. In this reasoning, the second premise contradicts the first part of the first premise.
For example,
1st premise: If it’s raining today, I will wear my raincoat.
2nd premise: I will not wear my raincoat.
Conclusion: It is not raining today.
See how Voxco can enhance your research efficiency.
In this article on inductive vs. deductive reasoning, it is safe to conclude that the advantages and characteristics of both reasoning can help you reach your research goals. The two has different approach, starting points, and ways of looking at the data, however, depending on your research goal each reasoning can give you the evidence you need to conclude the truth about your customers.
Voxco is an intuitive survey software that you can use to conduct inductive and deductive research. You can leverage robust functionalities to design surveys, collect data, and automate data analysis in real-time.
1. What is the difference between inductive and deductive reasoning?
Inductive reasoning involves moving from specific observations to broader generalizations, while deductive reasoning starts with general principles to reach specific conclusions.
2. How do inductive and deductive reasoning contribute to scientific methods?
Inductive reasoning helps in forming hypotheses and theories based on observations, while deductive reasoning is used to test these hypotheses and theories to draw specific conclusions.
3. Can you provide examples of inductive and deductive reasoning?
Sure! An example of inductive reasoning would be observing several instances of a phenomenon and generalizing a theory from them, whereas deductive reasoning involves starting with a theory and using it to make predictions or draw conclusions in specific cases.
4. What are the strengths and limitations of inductive reasoning?
Inductive reasoning allows for exploration and working with probabilities but has a limited scope as it’s based on specific observations and may not always apply universally.
5. How does deductive reasoning help in decision-making?
Deductive reasoning helps substantiate decisions by providing logical explanations and is reliable when the initial premises are true, aiding in making informed choices based on logical deductions.
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