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Gunning Fog Index Calculator

The Gunning Fog Index estimates the years of formal education needed to understand a piece of text on first reading.

When to use this

Use Gunning Fog when your goal is plain business writing — Robert Gunning created it for newsroom editors who needed a quick way to catch jargon. It is especially useful for evaluating financial reports, legal summaries, and corporate communications where multi-syllable words are easy to slip in unconsciously.

How it compares

Gunning Fog and SMOG both penalise polysyllabic words, but SMOG uses square-root scaling and is more conservative. Compared to Flesch–Kincaid, Fog is harsher on vocabulary and gentler on sentence length. Use Fog for business; use SMOG for health.

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How it works

The Gunning Fog index was created by Robert Gunning to identify text that is unnecessarily "foggy" or hard to understand.

It combines average sentence length with the percentage of "complex" words — words of 3+ syllables, with allowances for proper nouns, suffixes, and compound words.

A score of 12 corresponds to high-school senior level. Time and Newsweek typically score around 11.

Formula

FAQs

What is a complex word in Gunning Fog?

A word of 3 or more syllables, excluding proper nouns, familiar compound words (like "everywhere"), and words made polysyllabic by common suffixes (-ed, -es, -ing).

What score should I aim for?

Aim for under 12 for general audiences. Anything above 16 is considered very difficult to read.

Is Gunning Fog appropriate for business writing?

Yes. The Fog index was popularised by Robert Gunning specifically for business and journalistic writing.

Worked example

Input

The implementation requires consideration of consistency, observability, and serialization.

Output

Gunning Fog Index: 18.4 — Difficult (post-graduate).

Eleven words, but four are 3+ syllables. The Fog index multiplies the complex-word ratio by 100 and adds average sentence length, so dense vocabulary dominates the score.

Common pitfalls

  • Treats every 3+ syllable word as "complex", even when it is perfectly familiar (e.g., "actually", "interesting").
  • Proper nouns and common suffixes (-ed, -es, -ing) are supposed to be excluded; not all implementations honour this.
  • A single sentence with many complex words can drive a small sample to a misleadingly high score.
  • Optimising for low Fog can lead to choppy, simplistic prose that loses precision.

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