Last modified: 2026-06-22 06:24:47 (UTC)
Every claim in scientific writing should be supported by either citations to relevant sources or direct evidence from data or experiments. This principle is fundamental to maintaining credibility, enabling verification, and building on the accumulated knowledge of the scientific community.
Scientific writing aims to convey truthful, verifiable information. Unsupported claims undermine this goal in several ways:
When you make a claim, you are asking readers to accept it as true. They deserve to know why they should trust that claim.
Different types of claims require different types of support:
Claims about observable phenomena or data should be supported by:
Claims about concepts, models, or interpretations should be supported by:
Claims about appropriate methods or best practices should be supported by:
Not every statement requires citation. Well-established facts that are common knowledge within your field (e.g., “DNA is a double helix” in molecular biology) can be stated without citation. However, when in doubt, provide a citation—over-citing is preferable to under-citing.
A relevant citation is one that actually supports the specific claim you are making. Common problems with citation relevance include:
To ensure relevance:
Not all sources are equally reliable. Consider these factors when evaluating trustworthiness:
Peer-reviewed publications in reputable journals have undergone expert scrutiny. This doesn’t guarantee correctness, but it provides a baseline level of quality control.
Work from recognized experts and well-regarded institutions tends to be more reliable, though this should not be the sole criterion.
Findings that have been replicated by independent groups or that represent scientific consensus are more trustworthy than isolated claims.
Studies that:
are more trustworthy than those lacking these features.
Preprints can be valuable for accessing cutting-edge research, but they have not undergone peer review. When citing preprints:
Some sources typically lack the rigor needed for scientific writing:
Copying exact wording — whether from another writer or from your own earlier work, and even with a citation — can cause problems: plagiarism, if the wording is not clearly marked as a quotation, and a shift in voice that makes the text harder to read.
A few rules of thumb:
A related case is repetition within your own document. When a passage you are tempted to copy already appears elsewhere, cross-reference it instead of repeating it: this keeps a single source of truth and improves concision.
To effectively support your claims:
Machine learning models often perform poorly on small datasets.
Problem: This claim is stated as fact without any support.
Machine learning models often perform poorly on small datasets (Vapnik 1998; Hawkins 2004).
Improvement: Citations provide evidence for the claim.
Machine learning models often perform poorly on small datasets. Vapnik (1998) showed that the generalization error of learning algorithms typically decreases as training set size increases, and Hawkins (2004) demonstrated that complex models are particularly prone to overfitting when trained on limited data (Vapnik 1998; Hawkins 2004).
Improvement: The specific support each citation provides is explained.
Python is the most popular programming language for data science (Knuth 1984).
Problem: Knuth’s 1984 paper on literate programming doesn’t address Python or data science.
Python is the most popular programming language for data science (Stack Overflow 2024).
Improvement: The citation is to a current survey of programming language usage.
Supporting claims with appropriate citations and evidence is not optional—it is essential to scientific communication. It allows readers to verify your claims, understand the foundation of your arguments, and locate resources for further learning. Always ask yourself: “How does my reader know this is true?” If the answer isn’t obvious from your text, add a citation or present direct evidence.