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Central limit theorem - Wikipedia, the free encyclopedia
In probability theory , the central limit theorem ( CLT ) states that, given certain conditions, the mean of a sufficiently large number of independent random variables , each with finite mean and variance, will be approximately normally distributed . [ 1 ] The central limit theorem has a number of variants. In its common form, the random variables must be identically distributed. In variants, convergence of the mean to the normal distribution also occurs for non-identical distributions, given that they comply with certain conditions. In more general probability theory , a central limit theorem is any of a set of weak-convergence theories.
Benford's law - Wikipedia, the free encyclopedia
The distribution of first digits, according to Benford's law. Each bar represents a digit, and the height of the bar is the percentage of numbers that start with that digit. A logarithmic scale bar. Picking a random x position uniformly on this number line, roughly 30% of the time the first digit of the number will be 1.
By ESTHER LANDHUIS Persi Diaconis has spent much of his life turning scams inside out. In 1962, the then 17-year-old sought to stymie a Caribbean casino that was allegedly using shaved dice to boost house odds in games of chance. In the mid-1970s, the upstart statistician exposed some key problems in ESP research and debunked a handful of famed psychics.
Magician-turned-mathematician uncovers bias in coin flipping
Calculus
programing
Fundamentals of Mathematics
fractals
Number Theory