Pranesh

“The first [method] I might speak about is simplification. Suppose that you are given a problem to solve, I don't care what kind of problem-a machine to design, or a physical theory to develop, or a mathematical theorem to prove or something of that kind-probably a very powerful approach to this is to attempt to eliminate everything from the problem except the essentials; that is, cut is down to size. Almost every problem that you come across is befuddled with all kinds of extraneous data of one sort or another; and if you can bring this problem down into the main issues, you can see more clearly what you are trying to do an perhaps find a solution. Now in so doing you may have stripped away the problem you're after. You may have simplified it to the point that it doesn't even resemble the problem that you started with; but very often if you can solve this simple problem, you can add refinements to the solution of this until you get back to the solution of the one you started with.”
― Claude Shannon

“For a Bayesian, in fact, there is no such thing as the truth; you have a prior distribution over hypotheses, after seeing the data it becomes the posterior distribution, as given by Bayes’ theorem, and that’s all.”
― Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

“Be wary, though, of the way news media use the word “significant,” because to statisticians it doesn’t mean “noteworthy.” In statistics, the word “significant” means that the results passed mathematical tests such as t-tests, chi-square tests, regression, and principal components analysis (there are hundreds). Statistical significance tests quantify how easily pure chance can explain the results. With a very large number of observations, even small differences that are trivial in magnitude can be beyond what our models of change and randomness can explain. These tests don’t know what’s noteworthy and what’s not—that’s a human judgment.”
― Daniel J. Levitin, A Field Guide to Lies: Critical Thinking in the Information Age

“Probability theory naturally comes into play in what we shall call situation 1: When the data-point can be considered to be generated by some randomizing device, for example when throwing dice, flipping coins, or randomly allocating an individual to a medical treatment using a pseudo-random-number generator, and then recording the outcomes of their treatment. But in practice we may be faced with situation 2: When a pre-existing data-point is chosen by a randomizing device, say when selecting people to take part in a survey. And much of the time our data arises from situation 3: When there is no randomness at all, but we act as if the data-point were in fact generated by some random process, for example in interpreting the birth weight of our friend’s baby.”
― David Spiegelhalter, The Art of Statistics: Learning from Data