Every decision begins with data.
Whether conducting scientific research, analyzing experiments, interpreting medical evidence, evaluating public claims, or exploring machine learning, effective judgment depends on one essential skill: statistical reasoning.
Statistics and Data Science: Modern College Introduction provides a clear, rigorous, and contemporary introduction to the principles underlying today's data-driven world. Written for college students, researchers, and independent learners, the book develops statistical understanding from first principles while connecting classical methods with the computational approaches of modern data science.
The text begins with populations, samples, variables, measurement, descriptive statistics, probability, distributions, and the normal model. It then develops the central methods of statistical inference, including confidence intervals, hypothesis testing, t-tests, analysis of variance, correlation, regression, chi-square procedures, and nonparametric statistics.
From this foundation, the book extends naturally into methods increasingly important in contemporary research and data analysis: Bayesian statistics, logistic regression and classification, bootstrap and permutation methods, survival analysis, computational statistics, big data, machine learning, cross-validation, ensemble methods, neural networks, and the ethical responsibilities associated with statistical modeling and automated decision-making.
Throughout the book, emphasis is placed not merely on obtaining numerical answers but on understanding what statistical procedures mean, why they are appropriate, what assumptions they require, and what conclusions the evidence can legitimately support. Worked examples guide the reader from the research question and data through calculation, interpretation, and reporting. Figures, tables, practical examples, statistical references, software guidance, and datasets reinforce the connection between statistical theory and actual analytical practice.
Key features include:
- Clear, step-by-step explanations of fundamental statistical concepts
- Numerous worked examples and illustrations
- Descriptive and inferential statistics presented as a coherent method of reasoning
- Detailed treatment of t-tests, ANOVA, regression, chi-square, and nonparametric methods
- Bayesian inference, resampling, logistic regression, and survival analysis
- Computational statistics, big data, and modern machine learning
- Cross-validation, classification, ensemble methods, and neural networks
- Attention to reproducibility, uncertainty, bias, privacy, fairness, and statistical ethics
- Appendices containing statistical tables, a glossary, frequently asked questions, software guidance, and datasets for practice
- Suitable for university courses, scientific researchers, and independent study
The governing principle of the book is simple: statistics is not the mechanical production of numbers. It is the disciplined evaluation of evidence when knowledge is incomplete.
Learn to reason with data. Learn to think statistically.
Michael M. Nikoletseas, Ph.D. has devoted a lifetime to teaching, scientific research, and scholarly writing across statistics, neuroscience, philosophy, and the history of science. This textbook reflects decades of university-level teaching and the practical application of statistical methods in scientific research. His publications are held in major academic and research libraries throughout the world.
Revised 2026.
