Biostatistics Made Clear: A Student's Guide to Understanding Data in the Health Sciences
By Zerai Hagos, PhD
Most biostatistics textbooks force an unsatisfying choice: dense, formula-heavy treatments that assume mathematical maturity students don't yet have, or software manuals that teach which buttons to click without explaining why. Biostatistics Made Clear closes that gap for nursing, public health, and allied health students. Every worked example is independently verified rather than merely presented, and concepts carry forward across chapters instead of resetting each time — the same dataset that opens correlation reappears in regression, and a disease-screening problem solved with Bayes' theorem resurfaces in the diagnostic testing chapter, solved a second way, so students see the two methods agree. Fifty-three "Student Misconception" boxes name the exact wrong turns students make and correct them using the numbers already on the page, and the book is honest about where hand calculation stops being useful — multiple regression, logistic regression, and survival analysis are taught through correctly reading realistic software output, the actual skill a working researcher needs.
SPSS is integrated throughout with exact menu paths tied to each worked example, chosen as the gentlest on-ramp for students without programming backgrounds. Beyond running tests, the book covers the judgment that separates a technician from a literate researcher — study design, confounding, and the Bradford Hill criteria, plus a capstone chapter on real-world misuse of statistics including Simpson's paradox, p-hacking, and publication bias — material most introductory texts skip. It closes with a complete, usable reference package: a 194-term glossary, a full formula sheet, a verified answer key for all 240 practice questions, an SPSS quick-reference guide, and a page-accurate index, making it as useful for exam review as for a shelf reference afterward.
