Topic · Statistics & Econometrics
← All topicsCore Statistics
97 articles · 7 checkpoints · 76 deeper reads · 14 reference notes
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A compact way to describe a batch of numbers, minimum, first quartile, median, third quartile, maximum, and the standard picture (a box plot) that turns those five numbers into a shape you can compare across many groups at a glance.
The mistake of judging how likely something is from a positive test or signal alone, while ignoring how rare the thing being tested for was in the first place, a trap that makes even highly accurate signals produce mostly false alarms when what they're looking for is uncommon.
A practical workflow for picking which parametric distribution (normal, Student's t, lognormal, and so on) best describes a dataset, and estimating its parameters, instead of defaulting to the normal distribution out of habit.
A visual test for whether a sample of data plausibly came from a chosen distribution (like the normal), made by plotting the sample's sorted values against the values that distribution would predict at the same percentiles.
How to estimate a distribution's mean, variance, skewness and kurtosis from a finite sample, and, just as important, how uncertain each of those estimates is, since a moment computed from 60 data points is not the same thing as the true moment.
The short list of behaviours that show up in almost every traded market on earth, fat tails, volatility that clusters, direction that refuses to be predicted, and losses that hit harder than gains. Any model you build has to reproduce them, or it is describing a market that does not exist.
A way to measure how much knowing one variable tells you about another that, unlike correlation, catches any kind of dependence, curved, cyclical, or otherwise nonlinear, not just straight-line relationships.
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