[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODMyODQ","title","Applied Statistics with AI Hypothesis Testing and Inference for Modern Models","state","published","contactAuthorMethod","email","aboutText","

Applied Statistics with AI: Hypothesis Testing and Inference for Modern Models is a practical and conceptually focused guide to understanding how statistical thinking, probability, hypothesis testing, and statistical inference can be applied to modern Artificial Intelligence and Machine Learning systems.

Statistics provides the foundation for making reliable decisions from data, while AI and Machine Learning increasingly depend on statistical methods to evaluate models, quantify uncertainty, compare experiments, select features, and interpret results. This book brings these two areas together in a structured learning journey.

The book begins with the foundations of applied statistics, including data types, sampling, preprocessing, descriptive statistics, and visualization. It then introduces probability theory, conditional probability, Bayes' theorem, random variables, probability distributions, sampling distributions, and the Central Limit Theorem.

The statistical inference section covers estimation, Maximum Likelihood Estimation, Bayesian estimation, hypothesis testing, p-values, significance levels, Type I and Type II errors, parametric tests, and non-parametric methods.

The later chapters connect statistical inference directly to AI and Machine Learning. Topics include regression, model evaluation, cross-validation, confidence intervals, feature selection, PCA, regularization, A/B testing, statistical power, Bayesian Machine Learning, causal inference, uncertainty estimation in deep learning, and applied case studies.

The book concludes with statistical ethics, AI fairness, automated statistical inference, AI-driven hypothesis generation, and future research directions.

Designed for students, researchers, data scientists, AI practitioners, and professionals, this book aims to bridge the gap between traditional statistical theory and modern AI applications while encouraging rigorous, evidence-based interpretation of machine learning results.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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