Saturday, July 16, 2016

Wisdom Of The Week


(1) Order of Magnitude — “An order-of-magnitude estimate of a variable whose precise value is unknown is an estimate rounded to the nearest power of ten.” (related: order of approximation, back-of-the-envelope calculation, dimensional analysis, Fermi problem)

(1) Major vs Minor Factors — Major factors explains major portions of the results, while minor factors only explain minor portions. (related: first order vs second order effects — first order effects directly follow from a cause, while second order effects follow from first order effects.)

(1) False Positives and False Negatives — “A false positive error, or in short false positive, commonly called a ‘false alarm’, is a result that indicates a given condition has been fulfilled, when it actually has not been fulfilled…A false negative error, or in short false negative, is where a test result indicates that a condition failed, while it actually was successful, i.e. erroneously no effect has been assumed.”

(1) Confidence Interval — “Confidence intervals consist of a range of values (interval) that act as good estimates of the unknown population parameter; however, the interval computed from a particular sample does not necessarily include the true value of the parameter.” (related: error bar)

(2) Bayes’ Theorem — “Describes the probability of an event, based on conditions that might be related to the event. For example, suppose one is interested in whether a person has cancer, and knows the person’s age. If cancer is related to age, then, using Bayes’ theorem, information about the person’s age can be used to more accurately assess the probability that they have cancer.” (related: base rate fallacy)

(2) Regression to the Mean — “The phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement.”

(2) Inflection Point — “A point on a curve at which the curve changes from being concave (concave downward) to convex (concave upward), or vice versa.”

(3) Simpson’s Paradox — “A paradox in probability and statistics, in which a trend appears in different groups of data but disappears or reverses when these groups are combined.”

- Mental Models I Find Repeatedly Useful

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