Can an Existing Explanation That Doesn’t Work Well Be a Research Problem?

What If We Have an Explanation, but It Does Not Explain the Evidence Very Well?

A research problem does not always begin because nobody has proposed an explanation. Sometimes the literature already contains a theory, model, mechanism, or conceptual account that is supposed to explain a phenomenon. The difficulty is that important observations do not fit it particularly well.

Perhaps the explanation predicts a relationship that repeatedly appears only under some conditions. Perhaps it accounts for one population but struggles with another. Perhaps researchers keep encountering observations that require additional qualifications. Or perhaps a competing explanation seems capable of accounting for evidence that the established account leaves unresolved.

Can the inadequacy of an existing explanation itself provide the research problem?

Yes. Explanations are central to research precisely because they organize what researchers expect and help generate questions and hypotheses. When an explanation cannot adequately account for consequential evidence, understanding why it falls short can become a legitimate research problem.

An Explanation That Falls Short Can Create a Research Problem

When an Inadequate Explanation Becomes Worth Investigating

An Explanation Does More Than Describe What Happens

Description tells us what has been observed. Explanation attempts to account for how or why a phenomenon occurs, how concepts or variables relate, or what processes produce an observed pattern.

Theories are commonly understood as interrelated principles or explanatory hypotheses that organize and explain phenomena and may generate further empirical expectations. In research practice, theories and conceptual frameworks can help determine which relationships researchers investigate and what hypotheses they test.

This gives explanations scientific value, but it also makes their limitations important. If an explanation systematically struggles to account for relevant evidence, the mismatch can reveal something that researchers do not yet understand adequately.

The resulting problem is different from simply saying that there is a lack of understanding. Here, researchers already possess an explanatory account. The question is why that account does not work as expected.

“Doesn’t Work Well” Needs to Be Defined Precisely

You cannot establish a research problem merely by saying that a theory or explanation is imperfect. Scientific explanations are models of phenomena, and their usefulness and expected scope vary. Few explanations capture every observation under every imaginable condition.

You therefore need to identify the specific deficiency.

For example, an explanation might:

• predict an outcome that does not reliably occur;
• explain a relationship in one context but not another;
• fail to account for an important subgroup or class of observations;
• depend on an assumption that empirical evidence calls into question;
• omit a mechanism needed to explain observed variation;
• make predictions that are difficult to distinguish from those of a competing explanation; or
• require repeated qualifications to accommodate evidence that was not originally expected.

Each represents a different potential research problem. Identifying the type of inadequacy helps determine what evidence would actually improve the explanation.

A Surprising Observation Is Not Enough to Reject an Explanation

Suppose a theory predicts that X should be associated with Y, but one study finds no clear association. It is tempting to conclude that the theory has failed.

That conclusion may be premature.

The study may have inadequate statistical precision. The variables may have been measured poorly. The sample may differ from the population to which the explanation was intended to apply. An implementation may not correspond to the theoretical construct. Bias, analytical choices, or random variation may also contribute to the unexpected result.

Philosophy of science provides a useful warning here. Although hypotheses can imply observable consequences that are testable against evidence, interpreting an apparent failure is methodologically more complicated than treating every unexpected observation as a decisive falsification. Evidence can bear on an explanation without one anomalous result automatically determining that the entire explanation is wrong.

Your research problem therefore should not be “Theory X is wrong because Study Y disagreed with it.” A more defensible formulation asks why the evidence and explanation do not align and what evidence could distinguish among plausible reasons.

Repeated or Systematic Mismatches Are More Informative

An isolated anomaly may be interesting. A recurring pattern is usually more compelling.

Suppose multiple well-designed studies find that a predicted relationship appears reliably in one setting but weakens or disappears in another. That pattern can suggest that the original explanation has narrower boundary conditions than researchers assumed.

Or suppose an explanation predicts one mechanism, yet several different methodological approaches consistently point toward another process. The problem may then concern the adequacy of the proposed mechanism.

The more systematic the mismatch, the stronger the reason to investigate it. You still need to evaluate the quality and relevance of the evidence, but recurring discrepancies can indicate that something important about the explanation remains unresolved.

The Explanation May Be Incomplete Rather Than Wrong

Researchers should resist treating explanation testing as a simple choice between “correct” and “incorrect.”

An explanation can be useful while remaining incomplete. It may correctly identify one mechanism but omit another. It may work well for one class of cases while requiring modification elsewhere. It may describe the dominant process while failing to capture meaningful variation among individuals or environments.

For example, suppose a model explains adoption of a technology primarily through perceived usefulness. Evidence may consistently support the importance of usefulness while also showing that the model performs poorly in settings where access, institutional rules, or social pressures constrain people's choices.

The appropriate conclusion may not be that perceived usefulness is irrelevant. The more productive research problem may be that the existing explanation does not adequately incorporate the conditions that constrain whether perceived usefulness can influence behavior.

The Mismatch May Reveal a Boundary Condition

Some explanations are useful only under particular conditions. A boundary condition identifies circumstances under which a proposed relationship or mechanism changes, weakens, disappears, or otherwise does not operate as generally expected.

Discovering a boundary is not necessarily a failure of research. It can make an explanation more precise.

Suppose an established account predicts that greater autonomy increases motivation. Subsequent evidence suggests that the relationship is strong when individuals have sufficient expertise to exercise that autonomy but much weaker when they lack the knowledge needed to make effective choices.

The emerging research problem is not simply “the autonomy explanation is wrong.” It may be that the role of expertise in determining when autonomy affects motivation remains inadequately specified.

A study designed around that problem could test whether expertise genuinely changes the relationship and thereby clarify the conditions under which the explanation should be expected to apply.

The Problem May Be an Omitted Mechanism

Sometimes an explanation predicts outcomes reasonably well but researchers remain uncertain about the process producing them.

Imagine that an intervention consistently improves an outcome and an established account attributes the effect to mechanism A. New evidence suggests that mechanism B may also be operating, or that A does not change in the way the explanation predicts.

The empirical effect may still be real. What becomes uncertain is why it occurs.

This can create an important explanatory research problem because mechanisms affect how findings are interpreted, generalized, and used to make predictions. If researchers misunderstand the mechanism, they may expect an effect in situations where the necessary process is absent.

Competing Explanations Can Make the Problem More Precise

An explanatory problem becomes particularly useful when researchers can identify plausible alternatives.

Instead of asking only why Explanation A performs poorly, you may be able to compare predictions from Explanation A with those from Explanation B. The strongest design is one in which the alternatives lead to meaningfully different expectations that evidence can help distinguish.

If both explanations predict exactly the same observable outcome under your study conditions, collecting that outcome will not tell you much about which explanation is better.

This is why research questions and hypotheses should be logically connected to theory and previous evidence. A useful test does not merely collect more observations. It creates an opportunity for evidence to discriminate among plausible accounts.

Conflicting Evidence and Inadequate Explanation Often Overlap

These research problems can be closely related but should not be treated as identical.

If your starting point is that studies disagree rather than that an explanation fails to account for observations, you may be dealing primarily with conflicting evidence as the research problem. The literature may eventually show that the inconsistency exposes an explanatory limitation, but that connection needs to be demonstrated rather than assumed.

Check the Method Before Blaming the Explanation

When evidence does not fit an explanation, methodological limitations are always worth examining.

If a construct is measured badly, an apparent theoretical failure may actually be a measurement failure. If participants are selected in a way that restricts meaningful variation, a predicted relationship may become difficult to observe. If the design cannot adequately isolate the proposed mechanism, the resulting evidence may not provide a strong test of the explanation at all.

Conversely, if previous studies use methods that systematically favor one explanation, improved methodology may expose weaknesses that were previously hidden.

This means explanatory and methodological problems can become intertwined. If the evidence needed to evaluate the explanation is compromised by recurring design or measurement limitations, the more immediate problem may be the methodological weakness in the existing research.

An Explanatory Weakness Still Has to Matter

No explanation is perfect. If every minor mismatch automatically became a research problem, researchers could justify endless studies by pointing to increasingly trivial exceptions.

Ask what changes if the explanatory weakness is resolved.

Would it alter an important theoretical claim? Clarify when an influential model should be applied? Help researchers distinguish competing mechanisms? Explain a recurring empirical anomaly? Change predictions? Improve how later research is designed or interpreted?

If so, the inadequacy may be consequential. If the mismatch concerns an obscure exception with little effect on what anyone can understand, predict, or do, its significance may be weak.

You therefore still need to determine whether the research problem matters enough to justify investigation.

The Goal Is Better Explanation, Not Necessarily Destruction of the Old One

Researchers sometimes frame theory-oriented studies dramatically: an existing theory will be “disproved,” “overturned,” or “replaced.” That language is often stronger than the evidence warrants.

A more useful objective may be to test a particular prediction, clarify a boundary condition, evaluate an assumption, compare mechanisms, or determine whether extending an existing account improves its explanatory usefulness.

Scientific explanations develop through testing, criticism, modification, comparison, and sometimes replacement. A study can make a meaningful contribution by showing precisely where an explanation works and where it does not, even if the broader theoretical framework remains useful.

Turning an Explanatory Mismatch Into a Research Problem

The example is hypothetical. Its purpose is to illustrate the reasoning: observation does not fit explanation, plausible reasons for the mismatch are identified, and the research problem is framed around evidence that could improve the explanation rather than around a premature claim that the original account is simply wrong.

Common Mistakes When an Explanation Seems Not to Work

How to Build a Study Around an Explanation That Falls Short

If an existing explanation appears inadequate, resist beginning with the conclusion that it is wrong. First identify exactly what it fails to explain and what evidence would help distinguish among plausible reasons for the failure.

A useful problem statement should make the logic visible: Existing explanation A accounts for these observations, but it does not adequately account for this recurring pattern. Evidence suggests these plausible reasons for the mismatch. Resolving that uncertainty matters because it affects what the explanation can predict, explain, or legitimately claim.

Further reading may substantially change that formulation. You may discover that the explanation was never intended to apply to your case, that later research has already modified it, or that the apparent anomaly is weaker than you thought. In that situation, allow the research problem to change as you learn more rather than preserving a theoretical conflict that the evidence no longer supports.

Can You Defend an Inadequate Explanation as Your Research Problem?

Questions About Inadequate Theories and Explanations

An Explanation That Cannot Account for Important Evidence Can Become the Problem

How to Cite This Guide

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