03 · What You Need to Know
What Researchers Need to Understand About Consultant-Performed Analysis
Does Research Accountability Require Advanced Statistical Expertise?
Research accountability does not mean that every investigator must become an expert in every method used by a multidisciplinary team.
A clinical researcher may rely on a biostatistician for complex survival analysis. An educational technology researcher may collaborate with a specialist in multilevel modeling. A computational social scientist may require expertise in Bayesian estimation or network analysis.
Such collaboration is consistent with established research practice. Indeed, engaging an appropriately qualified specialist may be preferable to applying unfamiliar methods without adequate guidance.
However, specialist collaboration does not eliminate the need for the research team to understand the relationship between its evidence and conclusions.
The distinction is between possessing the technical expertise needed to perform an analysis and possessing sufficient methodological understanding to evaluate what the analysis supports.
What Is the Minimum Understanding a Researcher Should Have?
A researcher should ordinarily be able to explain the purpose of the analysis, the data used, the principal methodological choices, and the meaning of the reported results.
For example, when reporting a regression model, the researcher should understand which variable is the outcome, which predictors were included, what the estimated coefficients represent, and whether the findings concern association or support a defensible causal interpretation.
The researcher should also understand the principal assumptions and limitations relevant to the study's conclusions.
This does not mean memorizing every mathematical expression. It means understanding the analytical reasoning well enough to identify when a claim goes beyond the evidence.
The required knowledge may be greater when the researcher independently designed the analytical strategy or when statistical competence is an explicit learning outcome of an assessed thesis.
What Should You Be Able to Explain?
| Analytical component |
What the researcher should understand |
Question to ask the consultant |
| Research question |
What relationship, difference, prediction, or effect is being examined. |
What specific question does this analysis answer? |
| Analytical sample |
Which observations were included and why others were excluded. |
How was the final analytical sample determined? |
| Method selection |
Why the chosen procedure is appropriate for the design and variables. |
Why is this method preferable to reasonable alternatives? |
| Assumptions |
Which conditions affect the validity of the estimates or inferences. |
Which assumptions matter most, and how were they evaluated? |
| Results |
What the estimates, intervals, and statistical tests actually indicate. |
How should the principal estimates be interpreted in context? |
| Limitations |
What conclusions the analysis cannot establish. |
What alternative explanations or analytical weaknesses remain? |
| Reproducibility |
How the reported results were generated and can be checked. |
What documentation or scripts are available for verification? |
These questions provide a practical standard for meaningful engagement with the analysis. They do not replace the technical review that complex methods may require.
Why Must You Understand the Analytical Sample?
The conclusions of an analysis depend partly on which observations were included and how the analytical dataset was constructed.
A study may begin with 600 participant records but analyze only 487. That difference may reflect legitimate eligibility criteria, incomplete responses, missing variables, or other exclusions.
The researcher should understand the reasons for the difference and whether the exclusions could affect the findings.
For example, excluding students with incomplete engagement records may systematically remove individuals who participated less frequently in an online course.
If missingness is related to the outcome or predictors, the resulting analytical sample may differ meaningfully from the original population.
Researchers should not treat the final sample size as an arbitrary number supplied by statistical software.
Must You Know Why a Particular Statistical Model Was Selected?
Yes, at least at the level necessary to understand its suitability for the research question and data structure.
Suppose a consultant recommends a multilevel model because students are nested within classrooms. The researcher should understand that observations from students in the same classroom may not be statistically independent.
The researcher need not derive every estimation equation to appreciate why ignoring clustering could affect uncertainty estimates and interpretation.
Similarly, when a logistic regression is used for a binary outcome, the researcher should understand how the outcome differs from a continuous variable and how the resulting estimates should be interpreted.
Method selection should follow the study design and analytical objective rather than the consultant's familiarity with a particular software procedure.
How Much Must You Understand About Statistical Assumptions?
Researchers should understand the assumptions that materially affect the validity of the reported conclusions.
Different models rely on different conditions. Depending on the analysis, relevant concerns may involve independence, measurement, model specification, missingness, distributional assumptions, or the mechanism used to identify a causal effect.
Not every assumption can be tested directly from the observed data.
For example, a consultant may evaluate residual patterns in a regression model, but such diagnostics cannot establish that an observational association is free from unmeasured confounding.
Researchers should distinguish assumptions supported by diagnostic evidence from assumptions that remain substantive limitations.
When an assumption is questionable, ask whether alternative specifications or sensitivity analyses meaningfully change the findings.
Do You Need to Understand Statistical Significance?
Yes, especially when statistical significance is used to support a manuscript's conclusions.
A p-value does not indicate the probability that the research hypothesis is true. Nor does a result below a conventional threshold automatically establish practical importance or methodological validity.
The American Statistical Association's statement on p-values emphasizes that scientific conclusions should not depend solely on whether a p-value crosses a particular threshold.
Researchers should consider effect sizes, uncertainty, study design, measurement quality, and the substantive meaning of the findings.
For example, a statistically detectable difference in learning outcomes may be too small to justify a claim of meaningful educational improvement.
Conversely, a result that does not meet a conventional significance threshold does not necessarily establish that no effect exists.
What Should You Understand About Confidence Intervals?
Confidence intervals communicate uncertainty about estimated quantities under a specified statistical procedure and its assumptions.
A researcher should understand the parameter being estimated, the interval's units, and the range of values compatible with the data under the model.
A conventional 95% confidence interval does not mean there is a 95% probability that the fixed population parameter lies within the particular interval already calculated, under the usual frequentist interpretation.
For example, an estimated difference of 4 assessment points with a 95% confidence interval from 1 to 7 points suggests a range of plausible effect magnitudes under the analytical assumptions.
The researcher should consider whether those magnitudes are educationally meaningful rather than focusing exclusively on whether the interval excludes zero.
Must You Understand Every Formula and Algorithm?
Not necessarily. The appropriate depth depends on the researcher's role and the claims being made.
A substantive researcher may not need to reproduce the derivation of a maximum likelihood estimator or implement a Bayesian sampling algorithm independently.
However, someone reporting results from these methods should understand what is being estimated, the role of consequential assumptions, and how uncertainty is represented.
Where the interpretation depends on specialized mathematical details, a qualified statistician should remain involved in reviewing the relevant claims.
It is more defensible to acknowledge a division of expertise than to claim personal mastery of procedures one does not understand.
Scholarly accountability is compatible with collaboration, provided that responsibilities are explicit and the reported conclusions are appropriately verified.
What if the Consultant Uses a Method You Have Never Studied?
Unfamiliarity does not automatically make the method inappropriate.
Researchers may encounter methods that are better suited to their data than the procedures emphasized in their earlier training.
The consultant should explain the method in terms relevant to the research question, including why it was selected and what limitations accompany it.
Where necessary, the researcher may need additional training or independent methodological advice.
However, a method should not be accepted merely because it sounds sophisticated or produces favorable results.
Complexity is not evidence of validity. A simpler model may be preferable when it adequately addresses the research question and relies on more defensible assumptions.
What if the Consultant Changes the Original Analytical Plan?
Changes to an analytical plan may be legitimate when the original approach proves inappropriate or unforeseen data characteristics require adjustment.
However, the researcher should understand what changed, why it changed, and whether the revised analysis is exploratory or confirmatory.
For preregistered studies, deviations from prespecified analyses should be reported appropriately.
For example, replacing a planned model because of a defensible concern about clustered observations differs from repeatedly testing alternative specifications until a preferred result becomes statistically significant.
The consultant should document consequential decisions, and the researcher should evaluate their implications for the study's claims.
Analytical flexibility should not be concealed through retrospective descriptions suggesting that every decision was made before examining the data.
Can You Rely on the Consultant's Professional Credentials?
Qualifications are relevant when selecting a consultant, but they do not independently establish that a particular analysis is appropriate.
A statistician may possess advanced expertise while misunderstanding the study's substantive variables or data collection process.
Similarly, a technically correct analysis may be applied to a dataset whose provenance or measurement quality is inadequate.
Researchers should therefore evaluate the consultant's reasoning and outputs rather than rely exclusively on professional titles.
Independent verification may be especially appropriate when the analysis is consequential, difficult to reproduce, or inconsistent with other evidence.
What if the Consultant Cannot Explain the Results Clearly?
Statistical explanations may be technically demanding, but the consultant should be able to communicate the analytical purpose, assumptions, and interpretation at a level suitable for the research team.
Difficulty explaining a complex method does not automatically indicate incompetence. Nevertheless, persistent inability to justify consequential choices is a reason to seek clarification.
Researchers may request a methodological memorandum, annotated outputs, reproducible scripts, or a meeting focused on the principal decisions.
If important uncertainties remain unresolved, independent statistical review may be necessary before publication.
The appropriate response is not to invent an explanation for the manuscript or repeat technical language that nobody on the author team can defend.
What Documentation Should You Request?
The analytical record should be sufficient to understand how the reported findings were generated.
Depending on the method, useful materials include variable definitions, sample-selection rules, data transformations, missing-data procedures, model specifications, software versions, diagnostic results, and analytical scripts.
For complex analyses, documentation may also need to describe estimation settings, convergence problems, sensitivity analyses, and alternative models.
Not all documentation must be published, and access to underlying data may be restricted by privacy or contractual requirements.
However, the research team should retain an appropriate basis for evaluating and defending the analytical results.
The broader requirements for responsibly outsourcing statistical analysis include establishing these deliverables before the consultant begins work.
Does Reproducing the Results Prove the Analysis Is Correct?
No. Computational reproducibility and methodological validity are different properties.
Running the same code on the same dataset may reproduce an incorrect model or an inappropriate transformation.
For example, a reproducible script can consistently generate misleading results if it incorrectly codes the outcome variable.
Researchers should therefore evaluate whether the analytical decisions are justified, not merely whether the software produces identical numbers.
Reproducibility strengthens transparency and facilitates verification, but it does not replace methodological judgment.
Can a Consultant Interpret the Findings for You?
Yes. Statistical interpretation is a legitimate part of consultation and collaboration.
A consultant may explain coefficients, model diagnostics, uncertainty, and the limitations of statistical inference.
However, the research team should integrate those explanations with the study's theory, measurement, design, and substantive context.
A consultant who contributes substantially to interpretation may also warrant authorship consideration under applicable criteria.
Researchers should not present interpretations as their own independent work when doing so would misrepresent a contributor's intellectual role.
The distinction between legitimate research assistance and concealed intellectual contribution remains relevant to statistical reporting.
What if You Disagree With the Consultant?
Disagreement should be resolved through evidence and methodological reasoning rather than academic rank or contractual authority.
The researcher may understand the substantive context better, while the consultant may understand the statistical implications of particular assumptions more deeply.
Both perspectives can be important.
For example, a consultant may recommend excluding observations that appear statistically unusual, while the researcher knows that those observations represent genuine participants from a theoretically important subgroup.
The appropriate decision should consider data authenticity, study design, methodological justification, and the consequences for inference.
Where disagreement remains consequential, independent expert review may be preferable to selecting whichever interpretation supports the desired conclusion.
Are the Expectations Different for a Thesis or Dissertation?
They may be. A thesis or dissertation often serves as an assessment of the candidate's research competence.
Some universities permit statistical consultation but require the student to conduct specified analyses independently or demonstrate particular methodological competencies.
Other institutions may permit extensive specialist collaboration when appropriately approved and disclosed.
The candidate should understand the methods and conclusions sufficiently to meet the program's assessment requirements.
Being unable to explain the analysis during a dissertation defense may indicate a substantive weakness even when the consultant performed the calculations correctly.
The relevant institutional rules should be checked before delegating assessed analytical tasks.
What if You Discover That You Misunderstood the Analysis After Publication?
The appropriate response depends on whether the misunderstanding affected the manuscript's claims.
If the analysis was correctly performed but described inaccurately, a clarification or correction may be necessary.
If the misunderstanding led to unsupported conclusions, the authors should evaluate the consequences for the published findings and contact the journal when appropriate.
Not every interpretive error constitutes research misconduct. Honest mistakes should be distinguished from deliberate or reckless misrepresentation under the applicable framework.
However, the authors remain responsible for addressing material inaccuracies rather than attributing every problem to the consultant.
