Sublime
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given a number of observations, you can actually gain information by throwing information away!
Stephen M. Stigler • The Seven Pillars of Statistical Wisdom
person has many acquaintances, and all the pairwise probabilities don’t add up to a coherent model
Pedro Domingos • The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
A very simple and popular assumption is that all the effects are independent given the cause.
Pedro Domingos • The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Hypotheses typically estimate a given distribution parameter such as the mean or median. This is then used to build a histogram
Roman Zykov • Roman's Data Science: How to monetize your data
the probability of a cause (given an event) is proportional to the probability of the event (given its cause).
Sharon Bertsch McGrayne • The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy
Granted that more evidence is better than less, but how much better? For a very long time, there was no clear answer.
Stephen M. Stigler • The Seven Pillars of Statistical Wisdom
For k = 5, you have a 23% probability of seeing something statistically significant. For k = 10, that probability rises to 40%.
Ya Xu • Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
In even approximate equilibrium, the variability Darwin both required and demonstrated existed was in conflict with the observed short-term stability in populations.
Stephen M. Stigler • The Seven Pillars of Statistical Wisdom
important to understand the biases in your data and how that limits your conclusions.