This book presents a philosophical approach to probability and
probabilistic thinking, considering the underpinnings of probabilistic
reasoning and modeling, which effectively underlie everything in data
science. The ultimate goal is to call into question many standard tenets
and lay the philosophical and probabilistic groundwork and
infrastructure for statistical modeling. It is the first book devoted to
the philosophy of data aimed at working scientists and calls for a new
consideration in the practice of probability and statistics to eliminate
what has been referred to as the "Cult of Statistical Significance."
The book explains the philosophy of these ideas and not the mathematics,
though there are a handful of mathematical examples. The topics are
logically laid out, starting with basic philosophy as related to
probability, statistics, and science, and stepping through the key
probabilistic ideas and concepts, and ending with statistical models.
Its jargon-free approach asserts that standard methods, such as
out-of-the-box regression, cannot help in discovering cause. This new
way of looking at uncertainty ties together disparate fields -
probability, physics, biology, the "soft" sciences, computer science -
because each aims at discovering cause (of effects). It broadens the
understanding beyond frequentist and Bayesian methods to propose a Third
Way of modeling.
