In mathematics, the Khintchine inequality, named after Aleksandr Khinchin and spelled in multiple ways in the Latin alphabet, is a theorem from probability, and is also frequently used in analysis. Heuristically, it says that if we pick complex numbers , and add them together each multiplied by a random sign , then the expected value of the sum's modulus, or the modulus it will be closest to on average, will be not too far off from . Statement Let be i.i.d. random variables with for , i.e., a sequence with Rademacher distribution. Let and let . Then for some constants depending only on (see Expected value for notation). The sharp values of the constants were found by Haagerup (Ref. 2; see Ref. 3 for a simpler proof). It is a simple matter to see that when , and when . Haagerup found that where and is the Gamma function. One may note in particular that matches exactly the moments of a normal distribution. Uses in analysis The uses of this inequality are not limited to applications in probability theory. One example of its use in analysis is the following: if we let be a linear operator between two Lp spaces and , , with bounded norm , then one can use Khintchine's inequality to show that for some constant depending only on and . Generalizations For the case of Rademacher random variables, Pawel Hitczenko showed that the sharpest version is: where , and and are universal constants independent of . Here we assume that the are non-negative and non-increasing. See also Marcinkiewicz–Zygmund inequality Burkholder-Davis-Gundy inequality References Thomas H. Wolff, "Lectures on Harmonic Analysis". American Mathematical Society, University Lecture Series vol. 29, 2003. Uffe Haagerup, "The best constants in the Khintchine inequality", Studia Math. 70 (1981), no. 3, 231–283 (1982). Fedor Nazarov and Anatoliy Podkorytov, "Ball, Haagerup, and distribution functions", Complex analysis, operators, and related topics, 247–267, Oper. Theory Adv. Appl., 113, Birkhäuser, Basel, 2000. Theorems in analysis Probabilistic inequalities