Uncertainty: The Soul of Modeling, Probability & StatisticsThis 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. |
Contents
| 1 | |
2 Logic | 17 |
3 Induction and Intellection | 27 |
4 What Probability Is | 39 |
5 What Probability Is Not | 69 |
6 Chance and Randomness | 87 |
7 Causality | 113 |
Other editions - View all
Uncertainty: The Soul of Modeling, Probability & Statistics William Briggs No preview available - 2018 |
Uncertainty: The Soul of Modeling, Probability & Statistics William Briggs No preview available - 2016 |
Common terms and phrases
accept actual answer argument assume ball Bayesian believe better calculate called cancer causal cause certainty chance chapter claim conditional consider contingent course decision deduced definition discover discussion distribution equal error essence evidence example exist experiment fallacy false finite follows given gives guesses happen hypothesis idea implies important induction infinite instance interest irrelevant kind knowledge known least less limits logic mathematical matter means measured mechanical methods nature necessarily necessary never normal objects observed p-value parameters perhaps physical possible practice predictions premises probability problem proof proposition prove quantum mechanical question random reason regression relative relevant researcher score seen sense sequence sometimes statements statistical subjective Suppose testing theory things thought true truth turn uncertainty understanding universe usually values