You write a research paper from scratch. You cite every source properly. You paraphrase where paraphrasing is called for and quote where quoting is called for. You submit the paper to a plagiarism checker as a final safety check, and the tool flags four of your sentences. You did not copy any of them. You have never read the sources they matched against. But there they are on the report, marked as similar to previously published text.
That is the coincidence problem, and it happens on nearly every academic paper that gets scanned. Plagiarism checkers match sequences of words, and certain sequences of words show up across many documents by pure statistical coincidence, especially in technical writing, in common academic phrasing, and in short sentences with standard vocabulary. This piece walks through why these coincidental matches happen, what patterns you can expect to see flagged in your own writing, and what to do when they show up on your report.
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How plagiarism checkers actually match text
Plagiarism checkers work by comparing sequences of words in your submitted text against sequences in their index. The specific sequence length varies by tool. Most checkers look for exact matches of at least five to seven consecutive words, and some also flag paraphrased passages using semantic similarity. Phrasly’s tool checks against an index of more than 10 billion web pages and academic papers, which is comparable in scale to major institutional tools.
The math of this matching produces coincidence problems by definition. English has a finite number of common word sequences. Technical fields have even more finite vocabularies. Any given five-word sequence like “the results of the study” appears in millions of documents, and any six-word sequence like “as shown in figure 3 above” is standard boilerplate in scientific writing. When your paper uses these sequences (as any paper on a technical topic will), the tool flags them because the sequences match the index. The match is real. The plagiarism is not.
Where matches cluster in academic writing
Common academic phrases produce most of the coincidental matches on a typical paper. Phrases like “in this paper, we explore,” “the primary objective of this study,” “these findings suggest that,” and hundreds of similar constructions appear in tens of thousands of published works. They are not plagiarism by any reasonable definition. They are standard academic register that any writer in the field naturally produces.
Technical terminology produces the next largest share. Standard definitions, standard notation, standard descriptions of common methods (chromatography, ANOVA, regression analysis, PCR) all use largely the same words in the same order because those are the correct words for the concept. A methods section describing standard laboratory procedure will have very high overlap with any other paper describing the same procedure, and no author involved is copying anyone else.
The patterns of flagged sentences you can expect to see
If you scan any well-written academic paper, you will see four categories of flagged text show up regularly.
The first is short common phrases (three to seven words) that appear across many documents. These are the majority of matches on most papers and can usually be dismissed.
The second is standard technical terminology and definitions. Textbook definitions in your field will match textbook definitions in every other paper covering the same field, because the definitions are correct and there is only so much room to reword them.
The third is properly cited quotations. If you quoted a source with proper attribution, the quotation still shows up as matched text on the plagiarism report. The tool does not always know that the surrounding quotation marks and citation make the match legitimate. Most tools let you exclude quotations from the scan settings, which reduces this false-positive category significantly.
The fourth is your own reference list. If your paper cites 30 sources, and each citation contains author names, publication titles, and journal names that also appear on the works cited, the reference list itself typically shows a high similarity score. Excluding the reference list from the scan is standard practice.
Phrasly’s similarity checker produces a source-by-source report that lets you see which of these four categories each match falls into, and lets you decide which matches actually warrant attention. That granular view is the difference between a raw score and an interpretable report.
What to do when a match appears on legitimate original work
The first step is to look at the specific passage. If the flagged sentence is a common phrase, a standard definition, a properly cited quotation, or part of your reference list, no action is needed. The match is a mechanical artifact of how the tool works, not a signal of plagiarism.
If the flagged sentence is longer and matches a specific source you have not cited, the second step is to check whether the underlying idea in the sentence came from that source, even if the wording was independent. Sometimes writers reach the same phrasing independently because the topic constrains the words. Sometimes the wording overlap actually reflects an unattributed influence you did not realize you had absorbed. If it is the latter, adding a citation solves the problem cleanly, since the issue was attribution rather than the exact wording.
If a match is genuinely a case of accidental close paraphrase (you read the source, absorbed the phrasing, and reproduced it without realizing), the honest fix is to either quote and cite, or to rewrite the passage in a genuinely different way with the citation for the underlying idea. Rewriting until the tool stops flagging without addressing the source attribution is treating the symptom while leaving the problem intact.
The Coincidence Ceiling
Plagiarism checkers flag matches that exist. Whether those matches are plagiarism is a separate judgment that requires reading the report, understanding what was flagged, and knowing which categories of match are noise and which are signal. Most of the matches on a well-written original paper are noise. The tool cannot make that distinction on its own, and reacting to the top-line percentage as if it were a verdict misses the actual purpose of the report.
For anyone who wants to see the full breakdown of what was flagged and why, Phrasly provides the source-by-source view with direct links and highlighted passages for every match. That granular output is what makes it possible to separate the coincidental matches from the ones that actually need attention.
