Unexpected Finding in a Nursing Dissertation: Finding or Research Limitation?

An unexpected finding in a nursing dissertation can strengthen your work when you interpret it carefully instead of hiding it or automatically calling it a limitation. The key question is not whether the result surprised you. It is whether the pattern is supported by your data and whether the study design restricts the confidence, meaning or transferability of that pattern.

This guide gives you a clear way to separate findings from limitations. It also explains what to write when one result is both meaningful and affected by a methodological weakness. The focus is narrow: classifying and discussing an unexpected result. For broader chapter organisation, see our nursing dissertation discussion chapter structure.

Finding, limitation, anomaly or contradiction?

These terms answer different questions. Separating them early prevents vague discussion and helps readers follow your reasoning.

Term	Meaning	Best response
Unexpected finding	A supported result that differs from an expectation or much of the literature	Report, compare and interpret it
Research limitation	A study feature that restricts confidence or applicability	Name the mechanism and limit the claim
Outlier or anomaly	An unusual observation that differs markedly from the broader pattern	Check accuracy and apply the stated analysis plan
Contradictory account	A participant perspective that conflicts with another perspective	Examine variation instead of forcing consensus
Analytical error	A mistake in coding, calculation or method selection	Correct the analysis before reporting results

What is an unexpected finding in a nursing dissertation?

An unexpected finding is a result that differs from your prediction, conflicts with much of the previous literature or reveals a pattern that was not central to the original research question. It may emerge in quantitative, qualitative or mixed-methods work.

For example, a student might expect higher staffing levels to be associated with better patient satisfaction but find no statistically significant relationship. In a qualitative study, participants may describe a digital nursing intervention as increasing anxiety even though previous studies mainly report convenience and reassurance.

Neither result becomes a research limitation simply because it was unexpected. A finding describes what the data show. A limitation describes a feature of the research that restricts interpretation, credibility, precision or applicability.

Unexpected finding versus research limitation: the essential difference

Question	Unexpected finding	Research limitation
What does it describe?	A result or pattern in the analysed data	A constraint in the design, sample, measurement, analysis or context
Where is it reported?	Results or findings, then interpreted in the discussion	Limitations section and, where relevant, beside the affected interpretation
Does surprise make it weak?	No. Surprise alone says nothing about quality	No. A limitation must be linked to a specific consequence
What evidence supports it?	Tables, statistics, themes, quotations or integrated mixed-methods evidence	A defensible explanation of how a study feature may have influenced the result
What should the writer do?	Report it transparently and explore plausible explanations	State its likely effect without exaggerating or dismissing the whole study

A useful distinction is: the finding is the message from the data; the limitation defines how cautiously that message should be read.

Use a five-question decision test

1. Is the result visibly supported by the data?

Trace the claim back to the analysis. A quantitative claim should match the correct statistic, confidence interval, effect estimate or descriptive pattern. A qualitative claim should be grounded in coded material, an adequately developed theme and relevant participant evidence.

If the statement cannot be traced to the analysis, it may be speculation rather than a finding. Do not promote an interesting idea into a result merely because it sounds clinically plausible.

2. Was the analysis appropriate and completed consistently?

Check whether the selected method fits the research question, variables and dataset. For qualitative work, confirm that coding and theme development followed the stated analytic approach. For quantitative work, confirm assumptions, missing-data decisions and the direction of coding.

An analytical error is not an unexpected finding. Correct the analysis first. If the surprising result remains, it can then be reported honestly.

3. Is there a study feature that could reasonably have produced the pattern?

Look for a direct mechanism. A small or unrepresentative sample may reduce precision or transferability. A poorly worded questionnaire item may introduce measurement error. Social desirability may affect accounts of sensitive practice. A short follow-up period may miss a delayed outcome.

A vague statement such as “the sample was small” is not enough. Explain the consequence: for example, the small sample produced wide confidence intervals, so the apparent absence of an association may reflect low precision rather than evidence of no meaningful relationship.

4. Does credible literature offer a plausible interpretation?

Compare the result with studies that agree, disagree or examine a different population or context. Contradiction is not proof that your result is wrong. Differences may arise from setting, participant characteristics, measures, implementation or time period.

A balanced discussion should not search only for papers that rescue the result. Consider more than one explanation and indicate which is most consistent with your design and data.

5. Would the result still matter if the limitation were removed?

This counterfactual question is especially useful. If a more representative sample or stronger measure could plausibly produce the same pattern, you probably have a finding whose certainty is limited. If the result exists only because of a clear coding, measurement or recruitment problem, the methodological issue may dominate the interpretation.

When an unexpected finding is also affected by a limitation

Many real dissertation results belong in both categories. You should not force a false either-or decision. Report the result as a finding, then calibrate the claim by explaining the relevant limitation.

Use this sequence:

1. State the result: say exactly what was observed.
2. Compare it with the literature: identify agreement or contradiction.
3. Offer plausible explanations: connect them to context, theory or implementation.
4. Name the limitation: identify the precise design or data constraint.
5. Explain its effect: show whether it affects precision, credibility, causality or transferability.
6. End proportionately: state what can still be concluded and what requires further research.

The unexpected pattern is retained as a finding because it was consistently present across the analysed accounts. However, recruitment from one ward may have amplified experiences associated with that local team culture. The result therefore supports a context-specific interpretation but should not be transferred uncritically to other clinical settings.

This wording does not erase the result. It explains its evidential boundary.

Quantitative nursing dissertation example

Imagine that a survey examines whether student nurses’ simulation hours predict medication-calculation confidence. The analysis finds a weak, non-significant association, although much of the literature suggests simulation improves confidence.

The non-significant result is the finding. Potential limitations might include a small sample, restricted variation in simulation exposure, a self-report confidence scale or uncontrolled differences in prior clinical experience.

A weak discussion would say: “This result was unexpected and may be due to the small sample.” That sentence neither interprets the result nor explains the limitation.

A stronger version would be:

Simulation hours were not significantly associated with self-reported medication-calculation confidence in this sample. One explanation is that confidence reflects prior placement exposure and feedback quality as well as the number of simulation hours. Moreover, the narrow range of reported simulation exposure reduced the study’s ability to detect differences between participants. The result should therefore be interpreted as an absence of evidence for an association within this dataset, rather than proof that simulation exposure has no educational value.

Notice the boundaries. The writer does not claim causation, confuse statistical non-significance with no effect or dismiss the result as a mistake. For help presenting statistical outputs before interpreting them, use our guide to discussing SPSS results in a nursing dissertation.

Qualitative nursing dissertation example

Suppose most participants describe remote consultations as convenient, but several give detailed accounts of reduced privacy at home. The privacy concern may be unexpected, yet it is still a finding if it is supported by the data and developed through the stated analytic method.

Its importance should not be decided by counting quotations alone. The writer should consider depth, relevance to the research question, variation and the analytic approach. However, if recruitment relied on a digital patient group, people with poor internet access may be underrepresented. That recruitment limitation affects the range of perspectives available.

A suitable discussion might state:

Although convenience dominated participants’ accounts, privacy at home emerged as a distinct concern that complicated the assumption that remote access is uniformly empowering. This interpretation is supported by detailed accounts across more than one participant. Nevertheless, recruitment through an online group may have underrepresented patients who experience digital exclusion; therefore, the study may not capture the full range of access-related concerns.

If participants actively disagree with one another, that is a different analytical problem. See how to handle conflicting participant accounts without forcing artificial consensus.

Mixed-methods nursing dissertation example

In mixed-methods research, an unexpected finding in a nursing dissertation may appear as a mismatch between datasets. For example, questionnaire scores may suggest that patients are satisfied with discharge education, while interviews reveal uncertainty about medication changes. The mismatch is not automatically a flaw. It can be an integrated finding that shows how a summary score conceals differences in experience.

First, check whether both strands were analysed appropriately. Then compare what each method measures. A closed satisfaction item may capture overall approval, whereas an interview can reveal specific gaps. Next, examine limitations such as unequal sample sizes, different recruitment routes or poor timing between data collection stages.

Survey responses indicated high overall satisfaction with discharge education, yet interview accounts identified uncertainty about medication changes. Rather than treating these results as mutually exclusive, integration suggests that general satisfaction coexisted with a specific information gap. However, only a small subgroup completed interviews, so the qualitative accounts clarify a possible mechanism but cannot establish its prevalence across the survey sample.

This structure gives each dataset an appropriate role. It states the integrated finding, explains the apparent contradiction and then limits the claim that the smaller strand can support.

How to write an unexpected finding in a nursing dissertation

Report before you explain

Keep the results section close to the evidence. Present the relevant statistic, pattern or theme without importing a long explanation. Interpretation belongs mainly in the discussion unless your university requires an integrated findings-and-discussion chapter.

Use cautious, precise verbs

Prefer verbs such as “suggests,” “indicates,” “was associated with” or “was described by participants” when they match the design. Avoid “proves,” “caused” and “demonstrates that all nurses” unless the design genuinely warrants those claims.

Separate explanation from evidence

Signal when you are proposing an interpretation: “One possible explanation is…” Then support it with literature or a logical link to the study context. Do not write a possible explanation as though the data directly established it.

Discuss alternative explanations

A strong critical discussion considers rival accounts. The result may reflect a real contextual difference, a theoretical gap, an implementation issue, chance, measurement limitations or sample characteristics. Evaluate these possibilities instead of listing them.

Connect the result to nursing relevance

Explain whether the finding changes understanding, identifies a neglected patient experience, challenges an assumption or suggests a question for future study. Do not turn a small dissertation into a universal practice recommendation.

How to write the related research limitation

Each meaningful limitation should contain three parts:

• The specific constraint: what feature of the study was limited?
• The likely effect: how could it influence the result or its interpretation?
• The boundary or response: what claim remains defensible, or how was the problem reduced?

For example: “Because interviews were conducted by a researcher known to some participants, responses about placement support may have been influenced by social desirability. Reflexive notes and open-ended prompts were used to reduce premature assumptions, but positive accounts should still be interpreted cautiously.”

Avoid ritual limitation lists. Statements about time, word count or sample size add little unless you connect them to the evidence. Also avoid saying “there were no limitations.” Every design involves boundaries.

Common mistakes to avoid

• Calling every non-significant result a limitation. Statistical significance is a property of the analysis, not a judgement that the study failed.
• Hiding results that contradict the hypothesis. Selective reporting weakens transparency.
• Inventing post-hoc certainty. A plausible explanation is not automatically the confirmed explanation.
• Using sample size as a universal excuse. Explain its effect on precision, variation or transferability.
• Repeating the results section. Discussion should interpret, compare and evaluate.
• Overgeneralising clinical implications. Match recommendations to the study design and evidence strength.
• Confusing participant disagreement with poor data. Divergence can reveal meaningful differences in experience.

A paragraph template you can adapt

Use the following structure as a planning aid, not as text to copy mechanically:

This study found [precise unexpected result]. This differs from [relevant evidence or expectation], which reported [brief comparison]. One plausible explanation is [contextual, theoretical or methodological explanation], because [reason supported by evidence]. However, [specific limitation] may have affected [precision, credibility, causality or transferability] by [mechanism]. Consequently, the result suggests [proportionate interpretation], but it cannot establish [claim beyond the design]. Future research should [specific next step that addresses the uncertainty].

Before finalising the paragraph, check that every bracket has been replaced with a study-specific statement and that the literature comparison is accurate.

Final checklist

• Have you stated the unexpected result accurately?
• Can the claim be traced to a table, statistic, theme or quotation?
• Have you checked for analytical or coding errors?
• Have you compared the result with relevant evidence?
• Have you considered more than one plausible explanation?
• Is each limitation linked to a specific consequence?
• Have you avoided treating surprise as proof of weakness?
• Does your conclusion stay within the design’s boundaries?

Need help interpreting a difficult result?

If an unexpected result is making your findings or discussion chapter difficult to structure, our nursing dissertation specialists can help you evaluate the evidence, improve the logic and keep the interpretation proportionate. Contact Nursing Dissertation Service for support tailored to your brief and methodology.

Frequently asked questions

Is an unexpected finding automatically a limitation?

No. It is a result that differs from an expectation. It becomes linked to a limitation only when a feature of the design, sample, measurement or analysis restricts how the result can be interpreted.

Should I include a finding that contradicts my hypothesis?

Yes, provided the analysis is correct and the result is supported by the data. Report it transparently, compare it with existing evidence and avoid changing the hypothesis after seeing the result.

Can a result be both a finding and a limitation?

The result remains a finding, while a methodological constraint may limit confidence in it. Present the result first, then explain the limitation and its specific effect.

Where should unexpected findings be discussed?

Report them in the results or findings chapter and interpret them in the discussion. Mention the relevant limitation beside the interpretation and, if appropriate, in the dedicated limitations section.

Does a small sample invalidate an unexpected finding?

Not automatically. Its effect depends on the method and claim. It may reduce statistical precision or limit the range and transferability of qualitative accounts, but you must explain the actual consequence.

Conclusion

An unexpected finding in a nursing dissertation is not evidence that the project has failed. Treat it as a finding when it is supported by a suitable, consistently applied analysis. Treat a design or data constraint as a limitation when you can explain how it restricts precision, credibility, causality or transferability. When both apply, report the result transparently and set a proportionate boundary around the claim. That approach demonstrates critical thinking and produces a more trustworthy discussion.

References

Braun, V. and Clarke, V. (2021) ‘One size fits all? What counts as quality practice in (reflexive) thematic analysis?’, Qualitative Research in Psychology, 18(3), pp. 328–352. https://doi.org/10.1080/14780887.2020.1769238.

Levitt, H.M. et al. (2018) ‘Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology’, American Psychologist, 73(1), pp. 26–46. https://doi.org/10.1037/amp0000151.

Tong, A., Sainsbury, P. and Craig, J. (2007) ‘Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups’, International Journal for Quality in Health Care, 19(6), pp. 349–357. https://doi.org/10.1093/intqhc/mzm042.

Wasserstein, R.L., Schirm, A.L. and Lazar, N.A. (2019) ‘Moving to a world beyond “p < 0.05”’, The American Statistician, 73(sup1), pp. 1–19. https://doi.org/10.1080/00031305.2019.1583913.

Featured photograph by Priscilla Du Preez on Unsplash.