Inductive Reasoning – From Specific To General

11.10.22 Inductive vs. Deductive Time to read: 10min

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Inductive reasoning is a type of research done mostly under observation and different theories as a requirement at the end of the research process. You will understand how certain theories apply to your research work when formulating research questions and objectives. To do research effectively, you need to understand how theories apply to your work. You’ll learn data collection methods and practical research theories during this process, gaining valuable skills and knowledge.

Inductive reasoning in a nutshell

Inductive reasoning is like making an educated guess based on what you’ve seen or experienced. You look at specific examples or facts, and then you make a general rule or idea based on those. For example, if you’ve only ever seen white swans, you might use the inductive reasoning approach to guess that all swans are white. It’s not a 100% sure thing, but it’s a reasonable guess based on what you’ve seen.

Definition: Inductive reasoning

What is inductive reasoning? It is part of the methodology and a form of reasoning in which a general principle is drawn from a set of specific observations or cases. Inductive reasoning, also called inductive logic, moves from particular observations to broader generalizations, unlike deductive reasoning.

Inductive reasoning is commonly used in various fields such as science, where hypotheses are often formed based on observed data and then tested through experiments. This form of reasoning is a powerful method because it can lead to new discoveries in these fields. It is also prevalent in everyday decision-making. However, the conclusions reached through inductive reasoning are subject to revision or invalidation if further information becomes available. Since inductive reasoning is not definitive, it is typically evaluated based on the degree of probability rather than certainty.


Let’s say you have a bag of red, blue, and yellow marbles. You reach into the bag three times, and each time you pull out a red marble. Using inductive reasoning, you might conclude that all the marbles in the bag are red.

This conclusion isn’t guaranteed to be correct, but it’s a reasonable guess based on your specific experiences.

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Inductive research generally follows a sequence of stages designed for data collection, analyzing it, and building a theory. The specific stages can vary depending on the research discipline and methodology, but a simplified process of inductive reasoning might look something like this.

  • Observation
    The first step of inductive reasoning is making observations or gathering data like interviews or surveys.
  • Identifying patterns
    Search for patterns, trends, or commonalities within the information collected.
  • Generating hypotheses
    Generate hypotheses that you think will account for the phenomena you’ve observed.
  • Data collection for validation
    Collect additional data to validate or test the generated hypotheses through further observations, experiments, or the collection of additional qualitative data.
  • Analysis
    Analyze the new data in the context of your hypotheses. Use statistical tools or qualitative methods to evaluate whether the new data supports or contradicts the initial hypotheses.
  • Conclusion
    Draw conclusions about whether the data supported the hypotheses. At this stage of inductive reasoning, you may generalize.
  • Formulating theory
    If the hypotheses are consistently supported, they may be developed into a theory that explains the phenomenon in general terms, moving from specific instances to broader generalizations.
  • Reporting
    Finally, the results are usually compiled into a research paper to share with others. This often includes a discussion of the implications of the findings, limitations, and suggestions for future research.
  • Review and scrutiny
    In academic settings, the research undergoes peer review, where other experts in the field evaluate the methods and conclusions.

It’s important to note that inductive research is not a one-way street. Typically, the process is iterative, meaning that researchers go back and forth between stages as new data emerges, or new patterns are identified. Furthermore, the findings are typically considered more as strong suggestions rather than definitive conclusions. This is because inductive arguments are based on specific observations and may not be universally applicable.

Example of inductive reasoning

Let’s use the example of a researcher who wants to study why some high school students perform better in online classes compared to traditional in-person classes. The researcher uses an inductive reasoning approach throughout the research process.


  • Observation
    The researcher notices that some high school students seem to perform much better in online classes than in traditional classrooms. The observation could be based on grade data, student testimonials, or teachers’ observations.
  • Identifying patterns
    Upon looking at initial data, the researcher finds that students who perform better in online classes often mention flexibility, a quieter environment, and the ability to replay lectures as factors for their performance.
  • Generating hypotheses
    The researcher forms the hypothesis that students perform better in online classes due to increased flexibility, fewer distractions, and the ability to revisit course material at their own pace.
  • Data collection for validation
    The researcher decides to collect more data to validate these hypotheses. They might conduct surveys, hold interviews with students, and maybe even run a small experiment where student performance in both environments is compared.
  • Analysis
    The researcher analyzes the newly collected data, searching for evidence that supports or contradicts the hypotheses. They may use statistical methods if the data is quantitative, or thematic analysis for qualitative data.
  • Conclusion
    After analyzing the data, the researcher finds that there is significant evidence to suggest that the flexibility and the quieter environment of online learning do contribute to better performance. However, the ability to revisit course material did not have as strong an impact as initially thought.
  • Formulating theory
    Based on these findings, the researcher proposes a preliminary theory that high school student’s performance in online classes is strongly influenced by the flexibility and quieter environment that online learning offers.
  • Reporting
    The researcher compiles all of these findings into a research paper.
  • Review and scrutiny
    The paper is submitted to an academic journal, where it is peer-reviewed by other experts in the field.

By going through these stages, the researcher has moved from initial observations to a more generalized theory using inductive reasoning.

Types of reasoning

There are several types of inductive reasoning.

  • Inductive generalization
  • Statistical generalization
  • Casual reasoning
  • Sign reasoning
  • Analogical reasoning

Each type of inductive reasoning has its advantages and drawbacks and is appropriate in different contexts. Importantly, all forms provide strong but not conclusive evidence, meaning that they offer likely rather than guaranteed conclusions.

Inductive generalization

Inductive generalization involves creating broad conclusions based on a limited set of observations. This type of reasoning draws assumptions that if something is true for some members of a category, then it’s likely true for all members of that category. Furthermore, inductive reasoning generalizations can vary drastically in strength. This depends on the quality and number of arguments and observations used.


You observe that all the students from a particular school you’ve met are very polite. Based on these observations, you generalize that students from that school are generally polite.

Inductive generalizations are evaluated based on numerous criteria to determine their strength or reliability. Here are some of the key criteria:

  • Sample size: A substantial sample size is essential for reliable observations.
  • Random sampling: Using probability sampling techniques enhances the ability to extend your conclusions to a broader population.
  • Diversity of observations: To ensure external validity, your observations should capture a wide range of scenarios or conditions.
  • Counterevidence: If there is any evidence that goes against your observations, it weakens the accuracy and reliability of your generalization.

Statistical generalization

Statistical generalization is another type of inductive reasoning that is similar to inductive generalization. However, statistical data backs it, which is why it’s statistical reasoning. It uses a sample that is intended to be representative of a population to make general claims about the entire population.


A survey finds that 70% of sampled college students prefer online courses to traditional classes. You then generalize that many college students likely prefer online courses.

Causal reasoning

Another type of inductive reasoning is causal reasoning. The causal reasoning statement identifies relationships between cause and effect. It often involves determining a pattern and then providing a hypothesis for why that pattern exists, suggesting a cause-and-effect relationship. For causal reasoning, it’s especially helpful if there is a strong relationship between the cause and the effect. Generally speaking, this type of reasoning includes a causal link between the conclusion and the premise.


You notice that whenever you water a particular plant, it grows more the following week. You reason that watering the plant causes it to grow.

Sign reasoning

Sign reasoning involves drawing a conclusion about a purely correlational relationship based on one or more indicators or “signs”. One event may act as a “sign” that another event will occur or is currently occurring. In essence, the presence of one thing indicates the presence or occurrence of another.


If a student has high scores on practice exams, this is typically a sign that they will perform well on the actual exam.

Analogical reasoning

Analogical reasoning involves comparing two similar situations and proposing that what is true in one case is likely true in the other. This is why analogical reasoning is also called comparison reasoning. Analogical reasoning involves using knowledge from a similar context to make predictions or solve problems in a new context.


If Drug A is effective for treating a certain illness in adults, and Drug B has a similar chemical structure to Drug A, you might reason that Drug B could also be effective in treating the same illness in adults.

Inductive reasoning in research

In research, inductive reasoning is often used to build new theories or hypotheses based on observed data. This approach is particularly prevalent in fields like social sciences, psychology, and biology, among others. The goal of an inductive research method is to move from specific observations to broader generalizations or theories. Researchers using inductive reasoning frequently follow qualitative or mixed-methods research, although it can also be part of quantitative research. This form of reasoning plays a crucial role in academic writing, which is why it’s important to understand it. In the following, we will provide an inductive reasoning example, which demonstrates how to use this approach in research.


Let’s consider a researcher studying the impact of social media usage on mental health among teenagers.

  • Data collection
    The researcher conducts interviews with a group of teenagers to understand their social media habits and mental well-being.
  • Pattern identification
    It is noted that teenagers who spend more time on social media report higher levels of anxiety and depression.
  • Hypothesis
    The hypothesis is that increased social media usage is correlated with higher levels of anxiety and depression among teenagers.
  • Further data collection and analysis
    The researcher conducts a more extensive survey involving a larger and more diverse group of teenagers to test this hypothesis.
  • Theory development
    After analyzing the new data, the researcher finds that the hypothesis is strongly supported. As a result, they suggest a theory that there is a relationship between high social media usage and increased levels of anxiety and depression among teenagers.

The researcher publishes these findings, acknowledging that while the data proposes a strong correlation, it doesn’t definitively prove causation. Further research besides inductive reasoning, possibly using a deductive approach or experimental methods, could be conducted to test this theory more rigorously.

Inductive reasoning vs. deductive reasoning

Here’s a brief overview of the differences between inductive and deductive reasoning. We have a separate article on this topic if you want to read more about forms of scientific reasoning.

Aspect Inductive reasoning Deductive reasoning
Direction of reasoning From specific to general From general to specific
Starting point Observations for specific cases General principles or axioms
Goal Form generalizations or theories Derive specific, logically certain conclusions
Strength of conclusion Probable, but not certain Certain, if premises are true
Certainty of conclusion Likely but can be revised Certain if the premises are true
Method Empirical (involves data collection) Logical (involves application of rules)
Common uses Research, everyday decision-making. Formal logic, law, mathematics
Example All observed swans are white, so all swans might be white. All men are mortal. Socrates is a man. Therefore, Socrates is mortal.

Note: Inductive reasoning is frequently mistaken for deductive reasoning.


Just like any research method, this inductive reasoning has its limitations. They include the following:

  • You cannot be able to draw a conclusion based on an inductive approach. This is because it will not have any proof. However, you can invalidate it.
  • An inductive approach requires extensive research to gain findings.
  • A study with inductive reasoning requires enough resources, since occasionally the methods of data collection can be very demanding.
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Inductive reasoning requires you to apply more logic to your research work. To qualify as inductive findings, you will have to work from specific aspects to general ones. You might as well refer to inductive reasoning as a “bottom-up” type of research.

Inductive and deductive reasoning are both types of reasoning. However, they do differ in one aspect greatly.

  • Inductive reasoning moves from specific observations to general conclusions. This type of reasoning provides probable conclusions.
  • Deductive reasoning starts with general principles and arrives at specific conclusions. Certain conclusions, if the premises are true, are provided.

Inductive reasoning is a type of reasoning where you make general conclusions based on specific observations or examples. If you notice that the sun rises in the east every day, you might conclude that the sun always rises in the east.

Inductive reasoning is like making an educated guess based on what you’ve seen or experienced. You look at specific examples or facts, and then you make a general rule or idea based on those.


  • General principle: All men are mortal
  • Specific case: Socrates is a man
  • Specific conclusion: Socrates must be mortal

The deductive reasoning approach starts with a general principle and leads to a specific conclusion. Here is a deductive reasoning example.