03 · What You Need to Know
Observations Describe What Happened; Explanations Account for Why or How
Observation and Explanation Answer Different Questions
An observation tells researchers something about what was measured, recorded, or otherwise detected.
An explanation goes further. It attempts to account for why the observed phenomenon occurs, how its components are related, or what mechanism or process can produce the pattern.
Observation
A recorded or systematically documented feature, event, measurement, relationship, or pattern.
Explanation
An evidence-based account of why or how an observed phenomenon occurs, often connecting the observation to mechanisms, relationships, models, or broader theoretical understanding.
The National Research Council describes scientific explanations as accounts that link scientific knowledge with specific observations or phenomena. Explanations should fit the available evidence and remain open to scrutiny and competing accounts.
Not Every Research Question Requires the Same Kind of Explanation
Research does not always seek causal mechanisms.
Some studies aim primarily to describe a population, document an experience, characterize a process, reconstruct a historical development, estimate a relationship, interpret meaning, or predict an outcome.
Explanation becomes central when the research question asks why or how something occurs, what mechanism produces it, or which account best makes sense of the available observations.
This matters because researchers should not force explanatory claims onto designs intended primarily for description.
Patterns Often Create the Need for Explanation
Research may begin with an unexpected observation or recurring pattern.
Perhaps a phenomenon occurs more frequently under one condition than another. Two variables consistently move together. A policy produces different outcomes across institutions. Participants repeatedly describe the same tension in interviews.
The pattern creates a question: what could account for this?
At this stage, researchers do not yet possess an explanation merely because they have identified something interesting. They have identified something that may need explaining.
Researchers Draw on Existing Knowledge to Generate Possible Explanations
Scientific explanations do not normally emerge from observations in an intellectual vacuum.
Researchers use existing theories, prior studies, disciplinary knowledge, contextual understanding, and the observed evidence to generate possible explanations.
The National Research Council notes that explanations relate what is observed to what is already known. This is one way research contributes to cumulative knowledge: new observations are interpreted against an existing conceptual background rather than treated as isolated facts.
A Hypothesis Can Turn an Explanation Into Something Testable
A hypothesis can express a proposed explanation or a prediction derived from one in a form that can be examined empirically.
Suppose researchers propose that frequent low-stakes retrieval improves long-term learning because retrieving information strengthens later accessibility.
That explanation implies observable consequences. Students receiving retrieval opportunities should, under appropriately specified conditions, retain more material later than comparable students who merely restudy it.
The hypothesis helps connect the proposed explanation to evidence that could support or challenge it.
Predictions Give Explanations Empirical Consequences
A useful scientific explanation should do more than accommodate observations already known.
It should generate expectations about what researchers would observe if the explanation were adequate.
These expectations may concern new experiments, different populations, previously unmeasured variables, temporal patterns, mechanisms, or conditions under which an effect should disappear.
Scientific theories are especially valuable partly because they can organize existing observations while generating predictions that expose the explanation to further testing.
Researchers Design Investigations That Can Distinguish Among Explanations
If several explanations predict exactly the same observations under the study conditions, obtaining those observations will not tell researchers which explanation is better.
Good explanatory research therefore tries to create evidential leverage.
The National Research Council describes control of variables as one strategy for producing interpretable evidence and ruling out competing hypotheses. In experimental research, researchers may manipulate one factor while controlling others. In observational research, they may collect information across conditions, exploit temporal variation, use statistical adjustment, compare cases, or apply other designs suited to the question.
Qualitative and historical research may distinguish explanations through process evidence, contrasting cases, documentary evidence, temporal sequences, participant accounts, or other forms of systematic reasoning appropriate to the methodology.
There is no single universal experimental template for explanation.
Association Alone Does Not Automatically Explain Causation
Suppose researchers observe that students who spend more time on a learning platform tend to obtain higher grades.
Several explanations are possible. Platform use might improve learning. More motivated students might use the platform more often and also study harder elsewhere. Higher-performing students might find the platform easier to use. Instructors might encourage struggling students to use it more, creating still another pattern.
The association is a finding that needs explanation. It does not choose among these possibilities by itself.
Watch Out
A strong or statistically significant association does not automatically identify its cause. Causal explanation requires evidence capable of addressing plausible alternative accounts.
Inference Connects What Researchers Observe to What They Cannot Directly Observe
Many scientifically important phenomena cannot be observed directly.
Researchers infer mechanisms, past events, latent constructs, causal processes, and properties of objects that are inaccessible to direct observation by examining observable consequences.
The National Academy of Sciences has noted that scientific discovery frequently relies on indirect observation and inference. Historical sciences such as geology, astronomy, evolutionary biology, and archaeology routinely test explanations about processes that cannot simply be replayed for direct inspection.
Inference is therefore not an inferior substitute for observation. It is one of the central ways evidence becomes explanatory knowledge.
Alternative Explanations Must Be Taken Seriously
Researchers rarely strengthen an explanation merely by showing that it is compatible with the evidence. Several competing explanations may also fit.
A stronger question is whether the evidence distinguishes among them.
The National Research Council identifies evaluation against alternative explanations as a central feature of scientific inquiry. Researchers ask whether another explanation can account for the same observations, whether the reasoning contains flaws, and whether new evidence can discriminate between alternatives.
As alternatives become inconsistent with accumulating evidence, confidence in the surviving explanation may increase.
Evidence Can Support an Explanation Without Proving It Absolutely
Suppose an experiment produces exactly the pattern predicted by a hypothesis. That result can strengthen the proposed explanation.
It does not necessarily establish that no other explanation could ever account for the same evidence.
Scientific explanations can therefore become highly credible through repeated testing without becoming logically immune to revision. This is part of why empirical research generally produces evidence rather than absolute proof.
Unexpected Evidence Can Force an Explanation to Change
A useful explanation remains vulnerable to observations it does not accommodate.
If a predicted effect repeatedly fails to occur, if a supposed mechanism is absent, or if an alternative explanation predicts the evidence more successfully, researchers may need to modify or abandon the original account.
The National Research Council emphasizes that scientific theories and explanations are revised in light of new evidence and must withstand scrutiny before becoming widely accepted.
This is part of what it means to describe research as self-correcting.
Explanations Become Stronger When They Account for More Than One Observation
A useful explanation often does more than fit one result.
It may account for several previously disconnected observations, predict new phenomena, explain why an effect occurs only under certain conditions, or integrate findings generated by different research approaches.
Scientific theories are particularly valuable because they organize substantial bodies of evidence and provide explanations that apply across multiple instances.
This is why an explanation supported by a convincing body of evidence generally deserves more confidence than an explanation created to accommodate one isolated finding.
The Path From Observation to Explanation Is Usually Iterative
Textbook diagrams can make research look linear: observe, hypothesize, experiment, conclude.
Actual inquiry is often more recursive.
An observation generates a hypothesis. An experiment produces an unexpected result. Researchers revise the hypothesis. New measurements reveal another pattern. A competing explanation emerges. Further evidence distinguishes between them. The surviving explanation then generates new questions.
The National Research Council explicitly cautions against the idea that science follows one universal sequence called “the scientific method.” Scientists use multiple forms of reasoning, including pattern recognition, classification, generalization, deduction, and inference to the best explanation.
The movement from observation to explanation is therefore better understood as a cycle of evidence and reasoning than as a single one-way staircase.
