Philosophy of Science #8: Abduction, Deduction, and Induction by Charles Sanders Pierce
In this discussion, we will explore the three types of inferences argued by Charles Sanders Peirce. At this point, we have arrived at the modern-day practices for inferences by hypothesis testing. Who is Charles Sanders Peirce? Here comes GPT3.5 (Turbo)!
Charles Sanders Peirce (1839–1914) was an American philosopher, logician, mathematician, and scientist who is known for his contributions to semiotics, the study of signs and symbols. He is also known for his work in logic, pragmatism, and the philosophy of science. Peirce is considered one of the founders of the pragmatist movement, along with William James and John Dewey. He made significant contributions to several fields, including mathematics, statistics, and psychology. Peirce’s work had a profound impact on the development of modern philosophy and continues to influence thinkers in many fields today.
Charles argued for the use of three types of inferences. They were abduction, deduction, and induction. We often apply deduction and induction in our logic, but seldom heard of abduction. Don’t worry; you will remain at your seat (or stand where you are) and won’t get abducted!
So let’s go through one inference at a time.
Abduction
What is the abduction method? Coming back to the basket of apples in the supermarket, we observed that the green apple was picked on the twentieth round while red apples were picked for all nineteen rounds. This is a surprising fact because the ratio of red and green apples has been about the same for the past many months. But let’s assume that there was a local promotion on TV asking shoppers to buy green apples more — for the sake of good health — resulting in a surge in the purchase of green apples. If this local promotion was true and it existed, then there is reason to suspect that this local promotion did impact the sale of green apples, leading to the staff in the supermarket placing more green apples than red apples in the basket. However, there could be other reasons as to why the ratio is unbalanced. For example, there was a drought that affected most of the farms that grow red apples.
The objective of abduction is not to definitively design hypotheses. It is to minimize the cost of hypothesis testing by narrowing to possible reasons and increasing the available information given for the results of the hypothesis testing. According to Charles, the abduction method is a legitimate method of science.
Deduction
According to Charles, there are two types of deductions — the necessary deduction and the statistical deduction. We are familiar with the necessary deduction.
- If the twentieth pick is a green apple when all nineteen picks are red apples, then there are more red apples than green apples.
- Red apples are priced at 3 times more expensive than green apples.
- Therefore, revenue is expected to be higher when the twentieth pick is green and all nineteen picks are red apples.
In this deduction, we are saying that based on random rounds, we observe more red apples than green apples. And since red apples are priced 3 times more expensive than green apples, the revenue for red apples is much higher than the one for green apples in a random choice set. When we say random, there is an equal probability of picking any of the apples in the basket, regardless of their colour.
What about statistical deduction?
- 95% of 1000 apples are red apples.
- Drawing 20 samples from 1000 apples
- Hence, we can approximately arrive at 95% of the 20 samples are red apples.
In this statistical deduction, we are not looking at the representation of the population (ROP). In fact, we are looking at a derivation from the population to the samples. The first step is to derive the probability of getting a red apple based on a simple ratio. From this step, we determine the sample and decide the number of red apples in the ratio based on that probability. Unlike ROP by induction, we deduce based on facts gathered from the population and derive the truth based on the sample on hand.
Induction
Charles argued that induction works the other way around as compared to statistical deduction. Consider the following:
- 20 samples are drawn from a basket filled with 1000 apples.
- 5% of the 20 samples are green apples.
- Hence, we can approximately arrive at 5% of 1000 apples are green apples.
This is the method widely used to represent the population in our research work.
Let’s get GPT to write a use case for us to explain the use of these three methods.
Let’s consider a scenario where a detective is trying to solve a crime. The detective can use the abduction, deduction, and induction method of Charles Sanders Pierce to gather evidence and make logical conclusions.
Abduction: The detective uses the abductive method to form a hypothesis about the crime. For example, the detective may hypothesize that the crime was committed by someone who had motive, means, and opportunity.
Deduction: The detective uses the deductive method to test the hypothesis by gathering evidence and making logical deductions. For example, the detective may investigate the crime scene, interview witnesses, and analyze forensic evidence to deduce who had motive, means, and opportunity to commit the crime.
Induction: The detective uses the inductive method to draw conclusions from the evidence gathered. For example, the detective may use inductive reasoning to conclude that the suspect was at the crime scene based on witness statements, DNA evidence, and other factors.
By using the abduction, deduction, and induction method of Charles Sanders Pierce, the detective can make logical and informed conclusions about the crime and the perpetrator.
There you are. A use case so wonderfully written to help us understand these three methods advocated by Charles Sanders Pierce. Take note that the abduction is a hypothesis creation stage, the deduction is the data collection stage, and induction is the stage in which the detective performs the analysis of the data.
Charles had one important argument regarding these three methods. All three methods perform a self-correcting exercise, such that the outcome of the exercise is the truth. For example, at the deduction stage, the detective may have left out other pieces of evidence that might change his initial hypothesis. The analysis stage may show that the person who committed the crime is a woman rather than a man —an example of how the deduction is modified based on the results obtained during the induction stage.
In a nutshell, Charles advocated for the top-down and bottom-up approach in scientific inquiry, eventually leading to the truth.

Daniel started off his career as a senior list researcher with a British publishing firm. Back then, his role involved contact sourcing through the internet and performed data entry into the Microsoft Dynamic CRM system. (Microsoft Dynamic CRM 3.0) Progressively, he explored the option of using Visual Basic scripting within excel to automate the contact sourcing process.
He successfully developed and implemented the scripts, leading to 95% increase in data entry efficiency. He then moved on to take on the role of a CRM executive with Fuji Xerox Singapore.
As a CRM executive, he liaised with third party vendor for technical enhancement of the CRM system (Microsoft Dynamic CRM 4.0 and 365). He also performs functional enhancement of the CRM system for hundreds of end users.
His notable achievement was the development of the CRM boy that led to 98% improvement in data quality and data integrity in the CRM system. Following his Masters studies in Consumer Insight with Nanyang Business School, he took on the role of an Analytics instructor with Singapore Management University. He prepared class notes and technical walkthrough, and taught Analytics to the undergraduate students from various disciplines. Subsequently, he took on various roles as consultants in the consultancy, manufacturing and information technology industries in Singapore.

He travelled to Paris, London, Sri Lanka, Japan and Malaysia to fulfill his role as a consultant. The cultural and professional exchanges between local and overseas data analytics had given him a very good overview of the expectations and motivations from people around the world. He also had a chance to relocate to the United States for one year, particularly focusing on Operations Management.
Prior to his current freelance status, he took on the role of the Data Science Lead in a Singaporean software company. His primary role was to develop Artificial Intelligence using logic, data science and machine learning techniques through in-depth, full-stacked scripting. He also developed customized Reporting for his customers. In his point of view, 95% of today’s reporting can be automated, which can free up staff from daily manual work.

He holds a Bachelor of Science in Marketing (BSc. Marketing Pass with Merit) from Singapore University of Social Sciences (in which he graduated as a Valedictorian), a Master of Science in Marketing and Consumer Insights (MSc. Marketing and Consumer Insights) from Nanyang Technological University, a Doctor of Business Administration (DBA) from Swiss School of Business and Management.