Authors : Kato Samuel Namuene; Ndinge Nadia Mbella

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/ycw3bv5w

Scribd : https://tinyurl.com/hppr4cya

DOI : https://doi.org/10.38124/ijisrt/26apr077

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study presents a reproducible, AI-assisted framework for descriptive and univariate statistical analysis of ecological count data, integrating vibe data analysis with conventional manual methods using snapping frequency observations from 94 tree species in Korup National Park, Cameroon. Using Claude.ai to generate R statistical code through structured prompt engineering, we systematically compare classical parametric approaches (t-test, Z-test) with non-parametric alternatives (Wilcoxon signed-rank test, sign test, bootstrap confidence intervals) to determine the most appropriate analytical framework for forestry count data across four stages; exploratory data analysis, normality assessment, hypothesis testing, and outlier detection. Snapping frequency exhibited extreme positive skewness (5.087) and leptokurtic distribution (kurtosis = 36.725), Protomegabaria stapfiana (8 snappings; z = 3.41). Comparison of vibe analysis and manual analysis across 16 statistical outputs revealed complete numerical equivalence, with the AI demonstrating autonomous assumption-aware method selection without explicit instruction. While vibe analysis completed all stages within a single iterative session, mandatory validation through executed R code and analyst oversight remain essential. This framework provides forestry researchers with accessible, validated tools for rigorous, reproducible statistical analysis of non-normal count data.

Keywords : Descriptive Statistics; Univariate Analysis; Parametric Tests; Non-Parametric Tests; Normality Testing; Artificial Intelligence; R Programming; Data Visualization; Ecological Data Analysis.

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