Introduction

IMRaD—Introduction, Methods, Results, and Discussion— functions as an editorial public good: it standardizes manuscripts, facilitates peer review, and enables cross-article comparisons.
Moreover, starting with the scientific problem matters: formulating it well is equivalent to having already solved half of it (1).

This post does not propose abandoning IMRaD, but rather distinguishing between two complementary orders:

• Publication order (IMRaD): useful for evaluation and information retrieval
• Writing order (guided by findings): useful for aligning the narrative with what we actually learned and separating confirmatory from exploratory analyses (2,3).

In practice: first draft the key Results (estimates, confidence intervals, relevance), then the Discussion (plausibility, biases, sensitivity), followed by fully transparent Methods (clearly stating what was pre-specified, what changed, and why), and finally a concise, precise Introduction that frames the actual research question or hypothesis truly aligned with the results.
The final manuscript still adheres to the IMRaD format.

When to Use It—and When Not To

Recommended for: observational studies, secondary analyses, unexpected discoveries, and applied data science where iteration is inevitable.

Use caution / Not recommended for: Registered Reports, confirmatory trials with pre-specified statistical analysis plans (SAPs) (CONSORT), and systematic reviews with pre-registered protocols (PRISMA).
In these cases, maintain the primacy of the original plan and explicitly label any exploratory components as such (4,5).

Critical Objections (and Evidence-Based Responses)

“This encourages HARKing.”

Response: quite the opposite—if explicit labeling and open materials are required.
HARKing involves concealment; here we demand clear differentiation between confirmatory and exploratory analyses and full documentation of any changes (6–8).

“The Introduction loses its purpose.”

Response: it actually becomes stronger.
It frames the real problem illuminated by the evidence, without forcing a fictitious chronological narrative. The ICMJE promotes IMRaD for clarity—not for rewriting history (9).

“Editors require strict IMRaD compliance.”

Response: we preserve it.
Only the writing workflow changes, not the macrostructure. Moreover, TOP Guidelines and open practices enhance editorial trust (10).

“Unexpected results are overvalued.”

Response: demand sensitivity analyses, negative controls, replication, and avoid fetishizing p < 0.05.

Transparency Checklist

Conclusion

IMRaD structures and enables comparability across scientific articles.
Integrating a findings-driven writing approach offers a moderate, practical way to gain honesty, utility, and traceability—without sacrificing methodological rigor or editorial clarity.
In certain contexts—especially exploratory ones—it can be the difference between a narrative faithful to discovery and a fictitious chronology.

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References

1. Pólya G. How to solve it. Princeton University Press; 1945.

2. Kerr NL. HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review. 1998;2(3):196-217.

3. Gelman A, Loken E. The garden of forking paths: Why multiple comparisons can be a problem [Internet]. 2013. Available from: https://stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf

4. Schulz KF, Altman DG, Moher D. CONSORT 2010 statement. BMJ. 2010;340:c332.

5. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement. BMJ. 2021;372:n71.

6. Kerr NL. HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review. 1998;2(3):196-217.

7. Munafò MR, Nosek BA, Bishop DVM, et al. A manifesto for reproducible science. Nature Human Behaviour. 2017;1:0021.

8. Nosek BA, Ebersole CR, DeHaven AC, Mellor DT. The preregistration revolution. PNAS. 2018;115(11):2600-6.

9. International Committee of Medical Journal Editors. Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. 2019; Available from: http://www.icmje.org/recommendations/

10. Nosek BA, et al. Promoting an open research culture (TOP Guidelines). Science. 2015;348(6242):1422-5.

11. Wasserstein RL, Lazar NA. The ASA’s statement on p-values: Context, process, and purpose. The American Statistician. 2016;70(2):129-33.

12. Wasserstein RL, Schirm AL, Lazar NA. Moving to a world beyond “p < 0.05”. The American Statistician. 2019;73(sup1):1-19.

13. McShane BB, Gal D, Gelman A, Robert C, Tackett JL. Abandon statistical significance. The American Statistician. 2019;73(sup1):235-45.