Understanding Clinical Data Analysis

Ten chapters make up this Springer textbook, written for doctors, nurses and other health professionals who can already run the basic analyses on their clinical data but are left puzzled by the reasons behind each step. It also suits readers who feel swamped by the constant stream of new statistical techniques in journal articles and conference presentations.

Statistical software has made it far easier in recent years to carry out the calculations themselves. Understanding the reasoning, however, has received less attention. The book takes up that gap and explains why common concepts such as probability distributions, hypothesis testing, randomness, normality and scientific rigor are needed, and why the Gaussian curve so often confuses readers without a mathematical background.

The author's aim is to address these underlying questions directly, so that readers can make sense of the methods they encounter and the choices made behind them.