Inference is a cognitive process whereby we derive conclusions, assumptions, predictions, and explanations based on our interpretations of observable data.
The process of inferring something serves us well because it helps us make guesses and estimates, predict outcomes, and come to well-founded assumptions that can inform our decision-making.
It enables us to make sense of the world around us based on a combination of observation and prior knowledge.
For example, we make an inference when we notice a wet umbrella and raincoats in the lobby and infer that it is raining outside. In this instance, you haven’t actually observed the rain, but you come to your conclusion based upon data in your surrounds and some logical thinking.
However, inference can lead us astray because by definition it involves making assumptions based on available data, and these assumptions may be wrong.
25 Inference Examples
- Sherlock Holmes: Consider how Sherlock Holmes and similar detectives draw conclusions from small observations in their surrounds. A speck of mud on a person’s shoe, for example, could lead Holmes to infer the individual recently visited a specific location. There’s room for error here, but Sherlock feels confident based on his prior experience as an investigator and his strong deductive skills
- The Misleading Skier: You meet a guy on a dating app who talks about skiing all the time. They wear ski gear, they talk about famous skiers, and they talk about recent ski trips. So, you reasonably infer that they’re good at skiing. But when you go skiing with them, it turns out they’re terrible! Often, we need to be careful about how people construct their identity – especially on dating apps – because it can be misleading!
- Symptoms of Sickness: A doctor sees a patient who complains of frequent coughing a runny nose. They might infer that the patient has a cold. This inference is based on the doctor’s knowledge of common cold symptoms. But the doctor might need to pause and remember they have a responsibility to do some further investigating and not rely on inference alone, or at least, not yet.
- Emotional Intelligence: Emotional intelligence is based on inference. For example, you might see a friend crying and therefore infer that they are upset. Facial expressions and body language often serve as the foundation for such emotional inferences. If you’re good at making these inferences from subtle signals, you might be highly emotionally intelligent.
- The Confident Student: If a student studies diligently and performs well on practice tests, they can infer that they will likely receive a high grade on an upcoming exam.
- Predicting the Weather: Dark clouds gather in the sky, and you infer that it’s likely to rain soon. This inference is drawn from the observed weather patterns and past experiences, where you know these clouds tend to lead to storms.
- Predicting the Market: A financial analyst sees a pattern of increasing profits in a company’s reports and infers that the company’s stock value will soon rise. They use these inferences to pick stocks that they hope will help make money for their clients.
- Late for Work: You always get stopped at the traffic lights on the way to work. On this day, you’re stopped at the same traffic lights – but there’s a long, long queue in front of you. It seems there’s heavy traffic, and you know from experience that you’ll likely be late to work if there’s such a long line at these lights.
- The Election Pollster: Based on prepoll results, a political analyst might infer the outcome of an election. Later it turns out their inference is wrong, because they had a problem with their polling data. Not all inferences lead to correct outcomes.
- The Diet Inference: If someone avoids eating meat during a meal, one might infer that they are a vegetarian. However, you might be better off asking them directly, or withholding your assumptions, because maybe they just happened to want greens that day.
- The Successful Coach: A football coach, observing an opponent’s past tactics, may infer how they will play in an upcoming match. Based on this prediction, the coach trains his team on how to play in order to undermine the opposite team’s moves.
- The Job Satisfaction Inference: If an employee consistently works late and seems unhappy, a manager might infer they are dissatisfied with their job. The manager brings them into the office and asks them what’s wrong, and it turns out they were wrong: he’s just having trouble at home which is affecting his mood lately.
- The Clothing Inference: Looking out your window at the people walking around town, you see most people are wearing heavy coats and gloves. So, you infer that it is cold outside, and make the choice to wear a coat that day, without having actually felt the temperature outside yet.
- The Reading Interest Inference: If your niece often reads science fiction novels, you can infer that she has a keen interest in that genre. You use this information to buy her a science fiction book for her birthday.
- The New Relationship: Observing two people holding hands and laughing together, you may infer that they are in a relationship. They seem very flirtatious, so you assume it’s a new relationship. Later, you ask them, and find out they are in fact in a relationship, but they have been together for eight whole years! It seems your inference was half correct.
- The Gardening Neighbor: Seeing your neighbor often working in their garden, you infer they enjoy gardening.
- Music Preferences: If someone regularly wears a Nirvana t-shirt, you assume they are into 1990s grunge music. Or, perhaps, they just got the shirt from the thrift store. This assumption might need some more research before a good inference can be made!
- Cooking Skills: Tasting a well-cooked meal at a friend’s place, you infer they are a good cook. Of course, there are other possible explanations: maybe they only know how to cook one meal!
- Social Media Inference: If a person frequently posts about environmental issues on social media, you might infer they are environmentally conscious.
- Fitness Inference: Observing someone jogging every morning, you infer they are physically fit.
- The Morale Inference: If a sports team is cheering and high-fiving, a spectator might infer the team’s morale is high. Often, sports teams will intentionally do this. This is to psych out the opponents and make them infer something that may or may not be true.
- The Economic Recession: Economists might infer a potential recession if they observe consistent negative economic trends.
- Pet Ownership Inference: Seeing dog toys and a leash in someone’s house, you might infer that they own a dog. It turns out, they’re just minding their parents’ dog for the week while the parents go on a trip. Our inferences can lead us astray!
- The Sleep Pattern Inference: If a person is often yawning and looks tired during the day, you could infer that they are not getting enough sleep.
- The Distraction Inference: Noticing someone always doodling during meetings, you infer that they’re distracted and don’t pay attention. But really, they find that they concentrate better if they have something to fidget with.
Transitive Inference: A special type of inference, transitive inference refers to the ability to draw conclusions about the relationship between items based on indirect comparisons. For example, if you know that “John is taller than Mike” and “Mike is taller than Sarah”, using transitive inference, you can deduce that “John is taller than Sarah”, even if you’ve never compared John and Sarah’s heights directly.
Inference vs Observation
Observation and inference are two fundamental cognitive processes that enable us to make sense of the world, but they’re not the same.
Observation is the act of noticing or perceiving things using our senses. It involves gathering data firsthand through the sensory modalities of seeing, hearing, touching, tasting, or smelling (see: observational learning).
Observations provide us with direct facts or evidence about the world around us. They can be quantified and are usually verifiable (Lederman, Abd-El-Khalick, Bell, & Schwartz, 2002).
However, observing alone doesn’t help us with decision-making or coming to conclusions. For that, we’ll need inference.
An Example of an Observation: Noticing that “the sky is cloudy” is an observation because it directly describes a state of affairs as perceived through one’s senses.
Inference is the act of drawing conclusions based on observations and prior knowledge (Koslowski, 1996).
Unlike observations, inferences are not direct facts that we perceive with our senses.
Instead, they are mental leaps we make to predict, explain, or interpret the observed facts.
An Example of Inference: Stating that “it might rain” based on the observation of a cloudy sky is an inference because it involves predicting a future state of affairs based on observed facts and previous experiences.
Both observation and inference are crucial to our understanding and interaction with the world. They are fundamental to various fields, including science, where observation provides the empirical data, and inference allows us to interpret and make sense of that data (Koslowski, 1996).
Table Comparing Inference and Observation
|Definition||The act of noticing or perceiving things using our senses.||The act of drawing conclusions based on observations and prior knowledge.|
|Nature||Direct and empirical.||Indirect, involving interpretation or prediction.|
|Example||“The sky is cloudy.”||“It might rain.”|
|Role in Science||Provides empirical data.||Helps in the interpretation and understanding of data.|
|Verifiability||Usually verifiable as it’s based on firsthand sensory data.||May not be directly verifiable as it’s based on mental leaps.|
The Rules of Inference
The “rules of inference” are foundational principles used in logic and mathematics to arrive at valid conclusions based on given premises. They form the basis for logical reasoning and argumentation. Here are some of the most commonly recognized rules of inference:
- Modus Ponens (Affirming the Antecedent): If “P implies Q” is true, and “P” is true, then “Q” must also be true. For example, if “If it rains, then the ground gets wet” is true, and “It rains” is true, then “The ground gets wet” must be true.
- Modus Tollens (Denying the Consequent): If “P implies Q” is true, and “Q” is not true, then “P” must not be true. For example, if “If it rains, then the ground gets wet” is true, and “The ground is not wet” is true, then “It did not rain” must be true.
- Hypothetical Syllogism: If “P implies Q” is true, and “Q implies R” is true, then “P implies R” must be true. For example, if “If it rains, then the ground gets wet” is true, and “If the ground gets wet, then grass grows” is true, then “If it rains, then grass grows” must be true.
- Disjunctive Syllogism: If “P or Q” is true, and “P” is not true, then “Q” must be true. For example, if “It will rain today or it will be sunny” is true, and “It will not rain today” is true, then “It will be sunny” must be true.
- Conjunction: If “P” is true, and “Q” is true, then “P and Q” is true. For example, if “It is raining” is true, and “It is cold” is true, then “It is raining and it is cold” is true.
- Simplification: If “P and Q” is true, then “P” is true and “Q” is true. For example, if “It is raining and it is cold” is true, then “It is raining” is true and “It is cold” is true.
- Addition: If “P” is true, then “P or Q” is true. For example, if “It is raining” is true, then “It is raining or it is sunny” is true.
It’s important to remember that these rules of inference pertain to the structure of arguments and not their content. This means they’re concerned with whether an argument is logically structured, not whether it’s factually accurate.
The Ladder of Inference: How to Infer Something
The Ladder of Inference is a model that illustrates the cognitive process individuals undergo to make decisions or take actions based on their observations and assumptions.
This ladder demonstrates how we make inferences, but is generally used as a warning against “climing the ladder”. It shows how we take shortcuts in our thinking and form beliefs (and take actions) based on bad data.
This model was first proposed by Chris Argyris, a leading organizational psychologist, in his groundbreaking work on organizational learning (Argyris, 1976).
By being aware of this ladder, we’re less likely to fall into the trap of making false inferences based on incomplete facts.
1. Observing Reality: The first rung of the ladder involves observing the facts or reality of a situation without adding any interpretation or judgment.
Example: you get home and notice that your husband didn’t clean the house today, even though you asked him to very politely that morning.
2. Data Selection: As we ascend the ladder, we selectively focus on certain aspects based on our biases or past experiences, often ignoring other relevant information.
Example: You cherry-pick the data that seems most relevant, such as focusing on the fact you can see the video game console is out, suggesting he probably played games all day instead of cleaning.
3. Contextualizing Data: After data selection, we place this information within our framework of past experiences and beliefs, interpreting it subjectively.
Example: you might remember a previous time, two months ago, when your husband appeared to be being lazy that day. This informs your emerging assumption about him.
4. Making Assumptions: As we climb higher, we start making assumptions, often without confirming their validity. These assumptions typically derive from the context we’ve developed.
Example: You assume that your husband has spent all day being a lazy slob, and didn’t do any house cleaning.
5. Drawing Conclusions: We then draw conclusions from these assumptions, shaping our perception of the situation and dictating the actions we plan to undertake.
Example: You conclude that your husband is inherently lazy.
6. Adopting Beliefs: These conclusions often transform into personal beliefs that guide our future decision-making processes. This stage, known as the ‘reflexive loop’, reinforces our decisions, strengthening our biases.
Example: You start assuming that your husband will never do the house work even if you ask him and that he’s an unreliable person.
7. Taking Action: Lastly, we perform actions based on our beliefs, regardless of whether they are entirely grounded in reality. Consequently, the decisions made might not fully consider all relevant information.
Example: You stop asking your husband to do housework, but harbor a deep down resentment of him and start avoiding him altogether, causing a rift in the relationship.
This model prompts us to question our assumptions, broaden our data selection, and ensure our actions align with reality, not just our perception of it.
For example, with knowledge of the ladder of influence, the wife might interrupt her assumption that her husband is lazy, and start asking questions like: “did you have really busy day?” To which the husband will reply, “I am so behind on work, so I had to spend all day writing blog posts for the website. I didn’t even get a moment to put away that game console form last weekend!”
The Ladder of Inference model has been instrumental in facilitating dialogue and learning within organizations, allowing for the examination and challenge of assumptions and beliefs (Argyris, 1999). It encourages individuals to ‘descend the ladder’—returning to the raw data or observations, widening their field of data selection, and re-evaluating their conclusions.
I hate to cut this article on inference short, but I really do have to get around to cleaning this house before my wife comes home. I’d hate her to make a false inference about me being lazy! I hope you enjoyed these examples.
Argyris, C. (1976). Single-loop and double-loop models in research on decision making. Administrative Science Quarterly, 21(3), 363-375.
Argyris, C., & Schön, D. A. (1974). Theory in practice: Increasing professional effectiveness. London: Jossey-Bass.
Argyris, C. (1982). Reasoning, learning, and action: Individual and organizational. London: Jossey-Bass.
Argyris, C. (1985). Strategy, change and defensive routines. New York: Pitman.
Argyris, C. (1990). Overcoming organizational defenses: Facilitating organizational learning. Los Angeles: Allyn and Bacon.
Argyris, C. (1999). On Organizational Learning (2nd ed.). New York: Blackwell Business.
Koslowski, B. (1996). Theory and evidence: The development of scientific reasoning. Mass.: MIT press.
Lederman, N. G., Abd‐El‐Khalick, F., Bell, R. L., & Schwartz, R. S. (2002). Views of nature of science questionnaire: Toward valid and meaningful assessment of learners’ conceptions of nature of science. Journal of research in science teaching, 39(6), 497-521.
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]