I already have another book from Stephen Senn:
Senn, S.S. (2008). Statistical Issues in Drug Development (2nd ed.). Wiley-Interscience.
which offers a gentle overview of statistical issues in pharmaceutical research and drug development. Part 2 covers several hot topics in the statistical analysis of clinical trials, from their design to final reporting: treatment allocation, baseline and covariate adjustment, treatment effects, subgroup analysis, multiplicity of testing, missing data and intention-to-treat analysis, optimal sample size, multicentre trials, meta-analysis and various forms of RCTs (cross-over, n-of-1, sequential, dose finding).
Here, Stephen Senn originally tackles common paradoxes in probability, significance testing, clinical trials, bayesian vs. frequentist school of inference, and generalizability of results, among others. What is striking is that even if you are already familiar with those concepts, it is carried away by the quality and rigor of the discussion. Most of the time, Senn starts by giving an historical overview of one author's contributions and the scientific context of the time that allows us to appreciate the originality of the findings. This ought to be a delightful companion to more applied statistical textbooks.
A final quote:
Statistics are and statistics is
Statistics, singular, contrary to the popular perception, is not really about facts; it is about how we know, or suspect, or believe, that something is a fact. Because knowing about things involves counting and measuring them, then, it is true, that statistics plural are part of the concern of statistics singular, which is the science of quantitative reasoning. This science has much more in common with philosophy (in particular epistemology) than it does with accounting. Statisticians are applied philosophers. Philosophers argue how many angels can dance on the head of a needle; statisticians count them.
Or rather, count how many can probably dance. Probability is the heart of the matter, the heart of all matter if the quantum physicists can be believed. As far as the statistician is concerned this is true, whether the world is strivtly deterministic as Einstein believed or whether there is a residual ineluctable indeterminacy. We can predict nothing with certainty but we can predict how uncertain our predictions will be, on average that is. Statistics is the science that tells us how.