Observation is the most pervasive and fundamental practice of all the modern sciences, both natural and human. Its instruments include not only the naked senses but also tools such as the telescope and microscope, the questionnaire, the photographic plate, the notebook, the glassed-in beehive, and myriad other ingenious inventions designed to make the invisible visible, the evanescent permanent, the abstract concrete. Yet observation has almost never been considered as an object of historical inquiry in itself. This wide-ranging collection offers the first examination of the history of scientific observation in its own right, as both epistemic category and scientific practice.

Histories of Scientific Observation features engaging episodes drawn from across the spectrum of the natural and human sciences, ranging from meteorology, medicine, and natural history to economics, astronomy, and psychology. The contributions spotlight how observers have scrutinized everything—from seaweed to X-ray radiation, household budgets to the emotions—with ingenuity, curiosity, and perseverance verging on obsession. This book makes a compelling case for the significance of the long, surprising, and epistemologically significant history of scientific observation, a history full of innovations that have enlarged the possibilities of perception, judgment, and reason.

A daily glass of wine prolongs life—yet alcohol can cause life-threatening cancer. Some say raising the minimum wage will decrease inequality while others say it increases unemployment. Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they equally confidently claim it raises that risk. What should we make of this endless barrage of conflicting claims?

Observation and Experiment is an introduction to causal inference by one of the field’s leading scholars. An award-winning professor at Wharton, Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible. He draws his examples from clinical medicine, economics, public health, epidemiology, clinical psychology, and psychiatry to explain how randomized control trials are conceived and designed, how they differ from observational studies, and what techniques are available to mitigate their bias.

“Carefully and precisely written…reflecting superb statistical understanding, all communicated with the skill of a master teacher.”
—Stephen M. Stigler, author of The Seven Pillars of Statistical Wisdom

“An excellent introduction…Well-written and thoughtful…from one of causal inference’s noted experts.”
—Journal of the American Statistical Association

“Rosenbaum is a gifted expositor…an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference.”
—Psychometrika

“A very valuable contribution…Highly recommended.”
—International Statistical Review