Everything He Wants (The RHani Chronicles Book 3)

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You can find her online at her website sara-fawkes. Back to top. Get to Know Us. Amazon Payment Products. English Choose a language for shopping. Amazon Music Stream millions of songs. Amazon Advertising Find, attract, and engage customers. Amazon Drive Cloud storage from Amazon. Alexa Actionable Analytics for the Web. Sell on Amazon Start a Selling Account. AmazonGlobal Ship Orders Internationally. Amazon Inspire Digital Educational Resources. The justification on that scale of abstraction is elementary decision theory.

The hard part is finding such methods suitable for the kinds of data we now collect and showing that any particular method or class of methods satisfy those criteria. The authors avoid all of the hard parts. Even their discussion of randomized clinical trials -- one of the few methods that they actually come close to engaging -- adds nothing to the known limitations of such trials or contributes anything to how to improve their reproducibility. Now to some of the chapters. Chapter 1 tells us the job of philosophy for science is to provide norms and justify them.

It gives a banal summary of norms which it proposes to improve on: Be objective; be consistent with the data, "more or less"; the more empirical evidence for a theory, the more acceptable it should be; prefer theories with the greater explanatory scope; predictive success counts in favor of a theory. Chapter 2 wrangles with philosophers, current Norton or past Russell, Hume , who had or have reservations about causality in science. Chapter 4, "What's in a Correlation? There is, after all, a formula, but the authors say they are not concerned with that. They are talking "folk," not science. In passing, they implicitly distinguish correlation from dependence, but do not say what the difference is.

They give a short list of conclusions that might be drawn from correlation of event types A and B, including "no causal connection,' but do not describe the well-known ways correlations can be generated when there are no causal relations between A and B. There is nothing more in the chapter than the catalogue, with some brief examples. The uses and sources and kinds of distributions of variables are unmentioned; they do not discuss how to decide what distributions of values, and changes of distributions, are attributable to, or how to make use of them in causal inference; there is nothing about how to identify whether data points should be rejected, nor what to do about missing values.

Much is known about these matters, but it is not reported here. Chapter 6, "Ideal Conditions" observes that data are often messy and there are hard problems about extracting regularities from messy data, and reliably extrapolating from inside the lab to outside the lab. Some people, the authors observe, resort to probabilities, but, they object, that is not so helpful in deciding individual cases. What to do about these hard problems? Nothing said. Chapter 7, "One Effect, One Cause" tells the reader that isn't always so.

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