What question did this study set out to answer?

The central aim is to analyze the distribution of digital roots in Quranic vocabulary and compare it with other classical Arabic texts.

March 18, 2026Open Access

The Digital Root Fingerprint of the Quran: A Statistical Analysis of 3, 6, 9 Distribution Across Five Classical Arabic Texts

Key Points

The central aim is to analyze the distribution of digital roots in Quranic vocabulary and compare it with other classical Arabic texts.
Assigned Abjad values to each word in the Quran and classical texts.
Reduced values to single-digit roots (1-9) and measured distribution in {3,6,9}.
Used Monte Carlo simulation for p-value computation over 10,000 iterations.
Performed balance analysis to assess evenness of distribution across roots.
Quran showed a significant bias toward {3,6,9} with 38.23% (p=0.036).
Other classical texts did not meet significance thresholds.
Balance analysis revealed the Quran has the highest total and most even distribution at balance=0.836.
The identified bias is found only in function words, while content words showed no significant bias (p=0.22).

Abstract

This paper presents a statistical analysis of digital root distribution in Quranic vocabulary. Every word in the Quran was assigned its Abjad (Jummal) value, then reduced to a single-digit root (1-9). We measured the proportion of words falling in the set 3, 6, 9 across five classical Arabic texts: the Quran (78, 248 words), Sahih al-Bukhari (66, 345 words), Sahih Muslim (45, 884 words), the pre-Islamic Mu'allaqat (792 words), and Ibn Arabi's al-Futuhat al-Makkiyya (301, 798 words). Monte Carlo simulation (10, 000 iterations) was used to compute p-values. The Quran alone showed statistically significant bias toward 3, 6, 9 (38. 23%, p=0. 036). All other texts fell below the significance threshold. A balance analysis revealed the Quran simultaneously achieves the highest total AND the most even distribution across all three roots (balance=0. 836). The bias exists exclusively in function words; content words show no significant bias (p=0. 22). The frame is intentional, the content is free.

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