Enabling easy statistical significance testing for deep neural networks.
This repository contains analysis of churn in telephone service company (using IV and WOE), comparison of effect size and information value and quick tutorial how to use information value module (created for this analysis).
Minimal A/B Testing Library in PHP
A web application to design and evaluate the results of A/B tests.
Benchmark 19 retrieval architectures (vector, contextual, QnA, knowledge graph, LightRAG, hybrid, RAPTOR, PageIndex, BM25, metadata-filtered, temporal, rerank, HyDE, multi-query, late-interaction, SPLADE, agentic, plus two quantum-inspired rerankers) on your own docs. Citable evidence bundles, bootstrap CIs, significance tests.
A nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network
MRHCA: a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network
Check detected chart patterns against the rate the market moves that way anyway, so you can see which signals carry any information at all. Lift over a pattern-free baseline with cluster-robust intervals -- never against 50%.
Independent replication of the 5-minute Opening Range Breakout on QQQ (Zarattini & Aziz 2023, SSRN 4416622), stress-tested for execution costs. Break-even at ~2.2¢/share slippage; NQ confirmation filter significant per-trade (t=2.05) but 76% of its PnL is 2022 alone. Placebo control + bootstrap CIs.
Summary of Udacity's course on AB Testing
Used statistical measures to determine if the rate of re admissions for hospitals are high, if yes, what steps can be taken to bring the rate down.
Analyzes the results of A/B tests to determine if there is a statistically significant difference between control and treatment groups. It provides a structured approach for performing A/B tests, interpreting results, and making data-informed decisions. A valuable resource for marketers and product managers aiming to optimize user experience.
A model of the reliability of scientific findings as affected by the convention threshold for statistical significance and by various methods of reporting findings.
This R script analyses COVID-19 data focusing on death rates, age, and gender effects on mortality. It computes descriptive statistics, cleans data, calculates death rates, performs hypothesis testing, and evaluates claims regarding age and gender effects on death.
Nextflow workflow to run NCHG
Publication-grade, cost-sensitive credit default machine learning benchmark comparing Random Forest, Decision Tree, SVM, and Logistic Regression with Nadeau-Bengio statistical significance and an interactive dashboard.
This repository contains a detailed case study on an A/B test of LunarTech's homepage CTA button, using proxy data structured similarly to the company's real data.
Revenue optimization project for an online store. Applied ICE/RICE frameworks for hypothesis prioritization and conducted a rigorous A/B test analysis. Managed statistical significance, data filtering (outliers), and conversion rate modeling to drive data-led business decisions.
Statistically rigorous RAG evaluation framework featuring empirical 95% bootstrap confidence intervals, paired permutation hypothesis testing, and zero-API offline benchmarking.
Turn any network into an interactive knowledge graph — PageRank, HITS, betweenness and communities, each validated against a degree-preserving null model.
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