Last modified: 2026-09-26 05:55:30 (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.
Some phrases assert that a claim needs no support instead of supplying any:
The problem is the appeal, not the individual words. “Clearly” is fine as an adverb of manner (“the methods section clearly describes the sampling frame”), and “of course” is fine when it flags an assumption you have already established. Each becomes a problem only when it stands in for an argument you have not made.
These phrases do no work for the reader. A reader who already knows the result gains nothing from being told that it is familiar, and a reader who does not is told that the gap is their own fault, without being given any way to close it. They also cost the writer something. Working a step out is how you find out whether it holds; calling it obvious settles the question by assertion, and leaves any error in the step where it is. So if you cannot supply a derivation or a citation for a step, treat that as a reason to work the step out, not as a reason to call it obvious.
Replace such phrases with one of the following:
Deleting the phrase and keeping the claim is also usually an improvement: “the log-likelihood is a sum of three terms” makes the same claim about the log-likelihood as “the log-likelihood is obviously a sum of three terms”, and it invites the reader to check the claim rather than to defer to it.
Example 1 (Replacing an appeal to familiarity)
❌ Substituting the normal density into the likelihood gives the familiar three-term formula.
✅ For an independent sample \(x_1, \ldots, x_n\) from a normal distribution with mean \(\mu\) and variance \(\sigma^2\), substituting the normal density into the likelihood and taking logarithms gives \[ \ell(\mu, \sigma^2) = -\frac{n}{2}\log(2\pi) - \frac{n}{2}\log\left(\sigma^2\right) - \frac{1}{2\sigma^2}\sum_{i=1}^{n}\left(x_i - \mu\right)^2. \] The three terms are, in order:
- a constant that does not involve the parameters;
- a term that depends on \(\sigma^2\) but not on \(\mu\);
- the sum of squared deviations of the data from \(\mu\), divided by \(-2\sigma^2\).
The second version gives the reader the formula and where it came from, so they can check the algebra themselves. The first version asks them to take the writer’s word for it, and offers no help to anyone who has not seen the result before.
“Trivial” and “trivially” usually work the same way: they announce that a step is too small to write down, which relieves the writer of writing it down. Treat them as a prompt to check the step, and to write it out if it takes only a line.
The word has one defensible use, which is describing how a general expression reduces to a simpler one in a special case. For example, a weighted average reduces to an ordinary average when all of the weights are equal. Even in that use, “minimal case” or “degenerate case” names the situation more precisely, and avoids the dismissive tone that “trivial” carries.
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:
Quote and cite the source that said it, not a later source that quotes it. Check the wording and the page against the original.
❌ Mitchell (1997), as quoted in Goodfellow et al. (2016), defines …
✅ “A computer program is said to learn …” (Mitchell 1997)
Headings and sentences should name the topic, not its author: “A formal definition of machine learning”, not “Mitchell’s definition”. Follow a quotation with its citation alone, without commentary on who said it or where it was repeated.
When teaching material draws on another course, write your own version of the content rather than summarizing how that course presents it. Credit the source in a note at the start or end of the adapted section; in Quarto, a ::: notes div keeps the credit out of the slides.
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 your text does not answer that question, add a citation or present direct evidence.