Geostatistics Explained: An Introductory Guide for Earth Scientists

In this review of Geostatistics Explained: An Introductory Guide for Earth Scientists, the authors' clear, reader-friendly approach is the single best reason to consider this book. Aimed at students and researchers who find statistical concepts opaque, the book systematically walks from sampling design to multivariate techniques with an emphasis on practical understanding rather than abstruse math. The review highlights how the text demystifies statistical significance, provides intuitive graphical explanations of ANOVA, and makes advanced topics such as cluster analysis accessible for applied earth scientists.

Key Features

• Reader-focused explanations: Complex ideas are translated into plain language so students can apply methods without getting bogged down in formal proofs.
• Practical progression: The book starts with experimental and sampling design, ensuring readers understand how data collection affects later analyses.
• Clear coverage of errors: Type 1 and type 2 error and the implications of statistical significance are explained in a way that supports sound interpretation.
• Accessible ANOVA treatment: Graphical and stepwise explanations of analysis of variance make both basic and advanced designs easier to implement.
• Introduction to multivariate tools: Essentials of multi-dimensional scaling, cluster analysis and analysis of sequences are introduced for applied use.
• Non-parametric methods included: Practical discussion of tests such as chi-square offers alternatives when assumptions for parametric tests are not met.

Who It's For

This book is best suited to geology, geophysics and earth science undergraduates, graduate students, and researchers who need a practical grounding in geostatistics to analyse field and laboratory data without becoming statisticians. Its emphasis on sampling design and interpretation is particularly helpful for those responsible for experimental setup or for integrating datasets.

Those who should look elsewhere include readers seeking exhaustive mathematical derivations, formal proofs, or a specialist reference focused on a single technique; this is an introductory, application-oriented text rather than a comprehensive mathematical treatise.

Pros & Cons

Pros

• Clear, reader-friendly writing that makes probability and testing approachable for non-specialists.
• Emphasis on sampling and experimental design helps prevent common analysis mistakes.
• Good balance between introductory material and advanced topics such as multivariate analysis.
• Includes non-parametric tests and practical guidance for choices when assumptions fail.

Cons

• Not a substitute for a dedicated mathematical statistics textbook if rigorous derivations are required.

Specifications

Title	Geostatistics Explained: An Introductory Guide for Earth Scientists
Authors	Steve McKillup, Melinda Darby Dyar
Audience	Students and researchers in earth sciences
Scope	Sampling design, probability, ANOVA, regression, non-parametric tests, multivariate methods
Approach	Reader-friendly, application-oriented explanations
Includes	Graphical explanations, discussion of type 1 and type 2 error, cluster analysis

Our Verdict

For earth scientists who need to interpret and apply statistical methods to real data, this book offers strong value through clear explanations and practical guidance. It is especially useful for students and researchers focused on experimental design and applied analysis, though those needing formal proofs should supplement it with a mathematical statistics text.

Frequently Asked Questions

Is this book suitable for beginners?
Yes. The text is written to be accessible to readers with limited statistical background and emphasizes intuitive understanding.

Does it cover advanced multivariate techniques?
It introduces essentials such as multi-dimensional scaling and cluster analysis to provide applied familiarity rather than exhaustive coverage.

Will it help with field sampling design?
Yes. The book begins with a critical evaluation of experimental and sampling design to improve data collection and subsequent analysis.