Observation is the act of noting or detecting a phenomenon through the senses, often resulting in raw data or factual information. Inference is the process of drawing a conclusion or making a judgment based on those observations, often involving reasoning beyond the immediate data.
- Observation refers to an act of taking note of a phenomenon that is occurring around you. This can be done by simply using your senses (touch, sight, hearing, etc.) or even complex scientific instruments. Valuable data is obtained from this approach, providing a foundation for any scientific inquiry.
- Inference occurs after observation, where the observed data is utilized to form a logical conclusion. Whereas observation requires data gathering, influence requires reasoning, critique, and analysis skills to interpret and explain observations that have been made.
So, while observations offer raw data, your inferences allow you to make sense of these facts, thereby leading to deeper comprehension.
Observation vs Inference
What is Observation?
Observation involves the action of noting or detecting a phenomenon through human senses.
This process often acts as the initial step in various disciplines, especially within the sphere of scientific research, where facts and data drive knowledge advancement (Boyko, 2013).
Key characteristics include:
- Objective by Nature: The essential nature of observation is that it’s factual and objective, rather than the subjectivity of inference. Observation deals with the precise details and actual instances of a phenomenon, devoid of personal biases or interpretations. All observations seek to provide an unvarnished glimpse into the subject matter.
- Primary Data: Observations are based on direct evidence and firsthand experiences, not on deductive reasoning, inductive reasoning, or assumption. In other words, the observations you make are anchored in reality and are not secondary interpretations.
- Higher Certainty: Observations have a higher degree of certainty than inductions. Due to their first-hand and factual nature, observations are generally concrete and verifiable. You can consider them definitive points of information because they are grounded in empirical evidence.
- Observational Skills Required: Accurate detection and recording are crucial. The observer needs to be able to accurately identify phenomena and document them with precision, either through instruments or functioning senses. These skills fundamentally differ from those required for inference, which lend more towards analysis and reasoning.
What is Inference?
Inference involves drawing conclusions or making judgments based on observations. Unlike observations that provide raw data, inferences go a step further to make predictions, theories, or hypotheses.
The act of inferring is a cornerstone of intellectual and scientific endeavors (Sinharay, 2010).
Key characteristics include:
- Interpretative by Nature: Contrary to the objective nature of observation, inference is intrinsically interpretative and subjective. It relies heavily on individual judgment and analytical skills to draw logical conclusions from the data observed. The subjective element can introduce variability, as different people may infer differently from the same observed data.
- Secondary data: While observations depend on direct interaction with the subject matter, inferences leverage mental processes and logic to derive new information. This makes inference a powerful tool for making sense of secondary, complex, or unclear data.
- Lower Certainty: Inferences can have various levels of certainty, but are generally lower in certainty than observations because they by definition require interpreation. They can be speculative, and it’s possible that they may be proven wrong when tested. This is a crucial distinction from observations, which are usually verifiable and concrete.
- Analytical Skills Required: Analytical thinking and reasoning are the primary skills required for making inferences. Unlike observation, which needs accurate detection and documentation skills, inferences demand higher-order reasoning abilities. Being able to analyze data, identify patterns, and synthesize new insights are key components of effective inference.
See Also: The Multiple Types of Inference
Examples of Inference vs Observation
Example 1: Physics
- Observation: A physics student notices that a ball released from a height falls downward towards the ground, accelerating as it goes.
- Inference: From the observation, the student infers that the ball is under the influence of an unseen force — gravity — which is pulling it towards the ground and causing it to accelerate.
Example 2: Medical
- Observation: In a medical setting, a doctor observes a patient’s skin turning yellowish, indicating a possible health abnormality.
- Inference: A doctor, upon observing symptoms such as yellowing skin, abdominal pain, and loss of appetite, infers that the patient might have hepatitis. This assumption will then lead to further medical tests.
Example 3: Ecology
- Observation: An ecologist notices a decrease in the number of bees in a region over several months.
- Inference: Given the observed decline, the ecologist infers there might be a problem, such as habitat loss or environmental pollution, negatively affecting the bee population.
Example 4: Behavioral Psychology
- Observation: A psychologist observes that a child repeatedly refuses to participate in group activities, preferring to stay alone.
- Inference: By noting the child’s preferential solitude, the psychologist infers the potential presence of social anxiety or another psychological condition, prompting further diagnostic testing.
Example 5: Chemistry
- Observation: During an experiment, a scientist observes that when two particular chemicals combine, the solution changes color and emits heat.
- Inference: From these observations, the scientist infers that a chemical reaction is taking place between the two substances, leading to the creation of a new compound.
See More Examples of Inference and Examples of Observation
|Definition||Noting or detecting a phenomenon through the senses.||Drawing conclusions or making judgments based on observations.|
|Nature||Factual and objective.||Interpretative and subjective.|
|Basis||Direct evidence or firsthand experience.||Analysis, reasoning, or prior knowledge.|
|Dependence||Stands alone as raw data.||Relies on observations as a foundation.|
|Certainty||Generally concrete and verifiable.||Can be speculative and might be proven wrong.|
|Example||“The sky is blue.”||“It might rain later because the sky is dark.”|
|Uses||Provides the basic data in scientific experiments.||Helps form hypotheses, theories, or explanations in scientific research.|
|Change over time||Remains consistent unless the phenomenon changes.||Can change based on new observations or information.|
|Skill Required||Accurate detection and recording.||Analytical thinking and reasoning.|
Valid Causal Inference in Research
The distinction between inference and observation in psychology centers on the issue of being able to explain what causes human behavior (Koslowski, 1996). This is called a valid causal inference (McGue et al., 2010).
In essence, a valid causal inference means that the conclusions drawn about causal relationships from the observed data hold true beyond the scope of the initial observations. It sets out to determine the ‘why’ in addition to the ‘what’ that observations provide.
A simple observation might tell a researcher that individuals who receive cognitive behavioral therapy show diminished symptoms of anxiety.
However, a valid causal inference allows the researcher to go further, concluding that cognitive behavioral therapy actively helps in reducing anxiety symptoms (Cunningham, 2021). It enables the researcher to go beyond merely connecting the dots, permitting the legitimate prediction of outcomes.
To draw a valid causal inference, numerous potential confounding variables must be ruled out. This process offers a higher level of confidence that the result is due to the studied factor and not another extraneous variable.
Validity in causal inference can be affected by various factors, including the structure of the study, the methodology used, and the statistical tests applied. Randomized controlled trials, for instance, have higher potential for valid causal inference since they are designed to eliminate bias and isolate the cause-effect relationship.
Hence, making valid causal inferences is a significant part of psychological research that strives to explain human behaviors effectively. Yet, it extends beyond simple observations and delves into drawing connections between cause and effect, providing researchers with the ability to predict outcomes based on various influential factors.
Observation precedes inference and requires a completely different set of skills. Observing is more raw, based on using your senses to absorb information. Inference, on the other hand, is about taking observations (primary or secondary) and applying your cognitive skills to come to conclusions about information.
Boyko, E. J. (2013). Observational research—opportunities and limitations. Journal of Diabetes and its Complications, 27(6), 642-648.
Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press.
Koslowski, B. (1996). Theory and evidence: The development of scientific reasoning. Mass.: MIT press.
McGue, M., Osler, M., & Christensen, K. (2010). Causal inference and observational research: The utility of twins. Perspectives on Psychological Science, 5(5), 546-556.
Sinharay, S. (2010). An overview of statistics in education. In Penelope Peterson, Eva Baker, & Barry McGaw (Eds.), International Encyclopedia of Education (3rd Ed.), p. 1-11.
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]