Text Statistics

Text statistics reduce a block of writing to a handful of objective numbers — word count, character count (with and without spaces), line count, sentence count, paragraph count, average word length and reading time. Each one answers a different question: character count tells you how much space the text occupies, word count is a better proxy for how much content there is, and sentence and paragraph counts describe its structure.

Average word length is one of the most underused metrics — English prose averages around 4.5–5 characters per word, so a text with a much higher average signals dense, technical or jargon-heavy writing, while a much lower average suggests simple, conversational language. Paragraph count is also a useful signal: a document with one enormous paragraph is harder to read than the same words broken into ten, even though the word count is identical.

Readability formulas estimate how hard a text is to read from those raw counts. The Flesch Reading Ease score combines average sentence length and average syllables per word into a 0–100 scale; the Flesch-Kincaid Grade Level converts the same inputs into the US school grade needed to understand the text. They're approximations — they count syllables, not actual difficulty — but they're remarkably consistent across large samples.

The formulas reward short sentences and short words, which is why a score can make a text look worse than it feels: specialized vocabulary that your audience knows well counts against you regardless. Readability scores work best as a relative measure — comparing drafts, or checking that a user-facing doc sits below the grade 8–9 level recommended for general audiences — not as an absolute judgment of quality.

Reading time divides word count by an average reading speed — typically 200 words per minute for adult non-fiction — and rounds up. The average is real: studies of adult reading speed consistently land in the 200–250 wpm range for non-technical prose, though comprehension drops off at the faster end.

Individual speed varies enormously with familiarity with the topic, language proficiency and distraction, so the output is best treated as a planning figure: "about 8 minutes to read" manages reader expectations on a blog post, sets fair limits in a writing brief, and helps a team estimate how long a support article will take to work through — none of which require second-level precision.

Search engines don't have a word-count requirement, but content length correlates with ranking because longer articles tend to cover a topic more completely — the practical target for competitive search content is usually 1,500–2,500 words, with the word count serving as a rough proxy for coverage rather than a direct ranking factor.

Platform limits are exact, not approximate: a meta description gets cut off around 155–160 characters, posts on X (Twitter) truncate at 280 characters, and a subject line starts getting clipped in most inboxes around 60–70 characters. Character count is the only metric that matters at those boundaries — the difference between a fully visible call-to-action and a truncated one is often a handful of characters.

API docs have their own length norms. A reference entry should be short enough to scan — a few hundred words covering syntax, parameters and one example — while a conceptual guide can run to thousands. Tracking word count per section of documentation catches both the entry that grew bloated and the tutorial that's too thin to actually teach anything.

In technical writing more broadly, statistics are a QA tool: a sudden jump in average word length between drafts often means a new contributor wrote a section in denser language, and a paragraph count of one in a 3,000-word doc flags where structure needs to be added. The numbers don't judge the writing — they tell you where to look.