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Random download full version. A pseudorandomly generated bitmap. In the common parlance, randomness is the apparent lack of pattern or predictability in events. [1] 2] A random sequence of events, symbols or steps often has no order and does not follow an intelligible pattern or combination. Individual random events are by definition unpredictable, but since they often follow a probability distribution, the frequency of different outcomes over numerous events (or "trials" is predictable. [3] For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will occur twice as often as 4. In this view, randomness is a measure of uncertainty of an outcome, rather than its haphazardness, and applies to concepts of chance, probability, and information entropy. According to Ramsey theory, ideal randomness is impossible especially for large structures. For example, professor Theodore Motzkin pointed out that "while disorder is more probable in general, complete disorder is impossible. 4] Misunderstanding of this can lead to numerous conspiracy theories. [5] The fields of mathematics, probability, and statistics use formal definitions of randomness. In statistics, a random variable is an assignment of a numerical value to each possible outcome of an event space. This association facilitates the identification and the calculation of probabilities of the events. Random variables can appear in random sequences. A random process is a sequence of random variables whose outcomes do not follow a deterministic pattern, but follow an evolution described by probability distributions. These and other constructs are extremely useful in probability theory and the various applications of randomness. Randomness is most often used in statistics to signify well-defined statistical properties. Monte Carlo methods, which rely on random input (such as from random number generators or pseudorandom number generators) are important techniques in science, particularly in the field of computational science. [6] By analogy, quasi-Monte Carlo methods use quasi-random number generators. Random selection, when narrowly associated with a simple random sample, is a method of selecting items (often called units) from a population where the probability of choosing a specific item is the proportion of those items in the population. For example, with a bowl containing just 10 red marbles and 90 blue marbles, a random selection mechanism would choose a red marble with probability 1/10. Note that a random selection mechanism that selected 10 marbles from this bowl would not necessarily result in 1 red and 9 blue. In situations where a population consists of items that are distinguishable, a random selection mechanism requires equal probabilities for any item to be chosen. That is, if the selection process is such that each member of a population, say research subjects, has the same probability of being chosen, then we can say the selection process is random. [2] History In ancient history, the concepts of chance and randomness were intertwined with that of fate. Many ancient peoples threw dice to determine fate, and this later evolved into games of chance. Most ancient cultures used various methods of divination to attempt to circumvent randomness and fate. [7] 8] The Chinese of 3000 years ago were perhaps the earliest people to formalize odds and chance. The Greek philosophers discussed randomness at length, but only in non-quantitative forms. It was only in the 16th century that Italian mathematicians began to formalize the odds associated with various games of chance. The invention of calculus had a positive impact on the formal study of randomness. In the 1888 edition of his book The Logic of Chance, John Venn wrote a chapter on The conception of randomness that included his view of the randomness of the digits of pi, by using them to construct a random walk in two dimensions. [9] The early part of the 20th century saw a rapid growth in the formal analysis of randomness, as various approaches to the mathematical foundations of probability were introduced. In the mid- to late-20th century, ideas of algorithmic information theory introduced new dimensions to the field via the concept of algorithmic randomness. Although randomness had often been viewed as an obstacle and a nuisance for many centuries, in the 20th century computer scientists began to realize that the deliberate introduction of randomness into computations can be an effective tool for designing better algorithms. In some cases, such randomized algorithms even outperform the best deterministic methods. [10] In science Many scientific fields are concerned with randomness: In the physical sciences In the 19th century, scientists used the idea of random motions of molecules in the development of statistical mechanics to explain phenomena in thermodynamics and the properties of gases. According to several standard interpretations of quantum mechanics, microscopic phenomena are objectively random. [11] That is, in an experiment that controls all causally relevant parameters, some aspects of the outcome still vary randomly. For example, if a single unstable atom is placed in a controlled environment, it cannot be predicted how long it will take for the atom to decay—only the probability of decay in a given time. [12] Thus, quantum mechanics does not specify the outcome of individual experiments, but only the probabilities. Hidden variable theories reject the view that nature contains irreducible randomness: such theories posit that in the processes that appear random, properties with a certain statistical distribution are at work behind the scenes, determining the outcome in each case. In biology The modern evolutionary synthesis ascribes the observed diversity of life to random genetic mutations followed by natural selection. The latter retains some random mutations in the gene pool due to the systematically improved chance for survival and reproduction that those mutated genes confer on individuals who possess them. Several authors also claim that evolution (and sometimes development) requires a specific form of randomness, namely the introduction of qualitatively new behaviors. Instead of the choice of one possibility among several pre-given ones, this randomness corresponds to the formation of new possibilities. [13] 14] The characteristics of an organism arise to some extent deterministically (e. g., under the influence of genes and the environment) and to some extent randomly. For example, the density of freckles that appear on a person's skin is controlled by genes and exposure to light; whereas the exact location of individual freckles seems random. [15] As far as behavior is concerned, randomness is important if an animal is to behave in a way that is unpredictable to others. For instance, insects in flight tend to move about with random changes in direction, making it difficult for pursuing predators to predict their trajectories. In mathematics The mathematical theory of probability arose from attempts to formulate mathematical descriptions of chance events, originally in the context of gambling, but later in connection with physics. Statistics is used to infer the underlying probability distribution of a collection of empirical observations. For the purposes of simulation, it is necessary to have a large supply of random numbers —or means to generate them on demand. Algorithmic information theory studies, among other topics, what constitutes a random sequence. The central idea is that a string of bits is random if and only if it is shorter than any computer program that can produce that string ( Kolmogorov randomness) which means that random strings are those that cannot be compressed. Pioneers of this field include Andrey Kolmogorov and his student Per Martin-Löf, Ray Solomonoff, and Gregory Chaitin. For the notion of infinite sequence, one normally uses Per Martin-Löf 's definition. That is, an infinite sequence is random if and only if it withstands all recursively enumerable null sets. The other notions of random sequences include, among others, recursive randomness and Schnorr randomness, which are based on recursively computable martingales. It was shown by Yongge Wang that these randomness notions are generally different. [16] Randomness occurs in numbers such as log (2) and pi. The decimal digits of pi constitute an infinite sequence and "never repeat in a cyclical fashion. Numbers like pi are also considered likely to be normal, which means their digits are random in a certain statistical sense. Pi certainly seems to behave this way. In the first six billion decimal places of pi, each of the digits from 0 through 9 shows up about six hundred million times. Yet such results, conceivably accidental, do not prove normality even in base 10, much less normality in other number bases. [17] In statistics In statistics, randomness is commonly used to create simple random samples. This allows surveys of completely random groups of people to provide realistic data that is reflective of the population. Common methods of doing this include drawing names out of a hat, or using a random digit chart (a large table of random digits. In information science In information science, irrelevant or meaningless data is considered noise. Noise consists of numerous transient disturbances, with a statistically randomized time distribution. In communication theory, randomness in a signal is called "noise" and is opposed to that component of its variation that is causally attributable to the source, the signal. In terms of the development of random networks, for communication randomness rests on the two simple assumptions of Paul Erdős and Alfréd Rényi, who said that there were a fixed number of nodes and this number remained fixed for the life of the network, and that all nodes were equal and linked randomly to each other. clarification needed] 18] In finance The random walk hypothesis considers that asset prices in an organized market evolve at random, in the sense that the expected value of their change is zero but the actual value may turn out to be positive or negative. More generally, asset prices are influenced by a variety of unpredictable events in the general economic environment. In politics Random selection can be an official method to resolve tied elections in some jurisdictions. [19] Its use in politics is very old, as office holders in Ancient Athens were chosen by lot, there being no voting. Randomness and religion Randomness can be seen as conflicting with the deterministic ideas of some religions, such as those where the universe is created by an omniscient deity who is aware of all past and future events. If the universe is regarded to have a purpose, then randomness can be seen as impossible. This is one of the rationales for religious opposition to evolution, which states that non-random selection is applied to the results of random genetic variation. Hindu and Buddhist philosophies state that any event is the result of previous events, as is reflected in the concept of karma. As such, this conception is at odd with the idea of randomness, and any reconciliation between both of them would require an explanation. [20] In some religious contexts, procedures that are commonly perceived as randomizers are used for divination. Cleromancy uses the casting of bones or dice to reveal what is seen as the will of the gods. Applications In most of its mathematical, political, social and religious uses, randomness is used for its innate "fairness" and lack of bias. Politics: Athenian democracy was based on the concept of isonomia (equality of political rights) and used complex allotment machines to ensure that the positions on the ruling committees that ran Athens were fairly allocated. Allotment is now restricted to selecting jurors in Anglo-Saxon legal systems, and in situations where "fairness" is approximated by randomization, such as selecting jurors and military draft lotteries. Games: Random numbers were first investigated in the context of gambling, and many randomizing devices, such as dice, shuffling playing cards, and roulette wheels, were first developed for use in gambling. The ability to produce random numbers fairly is vital to electronic gambling, and, as such, the methods used to create them are usually regulated by government Gaming Control Boards. Random drawings are also used to determine lottery winners. In fact, randomness has been used for games of chance throughout history, and to select out individuals for an unwanted task in a fair way (see drawing straws. Sports: Some sports, including American football, use coin tosses to randomly select starting conditions for games or seed tied teams for postseason play. The National Basketball Association uses a weighted lottery to order teams in its draft. Mathematics: Random numbers are also employed where their use is mathematically important, such as sampling for opinion polls and for statistical sampling in quality control systems. Computational solutions for some types of problems use random numbers extensively, such as in the Monte Carlo method and in genetic algorithms. Medicine: Random allocation of a clinical intervention is used to reduce bias in controlled trials (e. g., randomized controlled trials. Religion: Although not intended to be random, various forms of divination such as cleromancy see what appears to be a random event as a means for a divine being to communicate their will (see also Free will and Determinism for more. Generation The ball in a roulette can be used as a source of apparent randomness, because its behavior is very sensitive to the initial conditions. It is generally accepted that there exist three mechanisms responsible for (apparently) random behavior in systems: Randomness coming from the environment (for example, Brownian motion, but also hardware random number generators. Randomness coming from the initial conditions. This aspect is studied by chaos theory, and is observed in systems whose behavior is very sensitive to small variations in initial conditions (such as pachinko machines and dice. Randomness intrinsically generated by the system. This is also called pseudorandomness, and is the kind used in pseudo-random number generators. There are many algorithms (based on arithmetics or cellular automaton) for generating pseudorandom numbers. The behavior of the system can be determined by knowing the seed state and the algorithm used. These methods are often quicker than getting "true" randomness from the environment. The many applications of randomness have led to many different methods for generating random data. These methods may vary as to how unpredictable or statistically random they are, and how quickly they can generate random numbers. Before the advent of computational random number generators, generating large amounts of sufficiently random numbers (which is important in statistics) required a lot of work. Results would sometimes be collected and distributed as random number tables. Measures and tests There are many practical measures of randomness for a binary sequence. These include measures based on frequency, discrete transforms, complexity, or a mixture of these, such as the tests by Kak, Phillips, Yuen, Hopkins, Beth and Dai, Mund, and Marsaglia and Zaman. [21] Quantum nonlocality has been used to certify the presence of genuine randomness in a given string of numbers. [22] Misconceptions and logical fallacies Popular perceptions of randomness are frequently mistaken, and are often based on fallacious reasoning or intuitions. A number is "due" This argument is, In a random selection of numbers, since all numbers eventually appear, those that have not come up yet are 'due' and thus more likely to come up soon. This logic is only correct if applied to a system where numbers that come up are removed from the system, such as when playing cards are drawn and not returned to the deck. In this case, once a jack is removed from the deck, the next draw is less likely to be a jack and more likely to be some other card. However, if the jack is returned to the deck, and the deck is thoroughly reshuffled, a jack is as likely to be drawn as any other card. The same applies in any other process where objects are selected independently, and none are removed after each event, such as the roll of a die, a coin toss, or most lottery number selection schemes. Truly random processes such as these do not have memory, which makes it impossible for past outcomes to affect future outcomes. In fact, there is no finite number of trials that can guarantee a success. A number is "cursed" or "blessed" In a random sequence of numbers, a number may be said to be cursed because it has come up less often in the past, and so it is thought that it will occur less often in the future. A number may be assumed to be blessed because it has occurred more often than others in the past, and so it is thought likely to come up more often in the future. This logic is valid only if the randomisation is biased, for example with a loaded die. If the die is fair, then previous rolls can give no indication of future events. In nature, events rarely occur with perfectly equal frequency, so observing outcomes to determine which events are more probable makes sense. However, it is fallacious to apply this logic to systems designed to make all outcomes equally likely, such as shuffled cards, dice, and roulette wheels. Odds are never dynamic In the beginning of a scenario, one might calculate the probability of a certain event. However, as soon as one gains more information about the scenario, one may need to re-calculate the probability accordingly. In the Monty Hall problem, when the host reveals one door that contains a goat, this provides new information that needs to be factored into the calculation of probabilities. For example, when being told that a woman has two children, one might be interested in knowing if either of them is a girl, and if yes, what is probability that the other child is also a girl. Considering the two events independently, one might expect that the probability that the other child is female is ½ (50. but by building a probability space illustrating all possible outcomes, one would notice that the probability is actually only ⅓ (33. To be sure, the probability space does illustrate four ways of having these two children: boy-boy, girl-boy, boy-girl, and girl-girl. But once it is known that at least one of the children is female, this rules out the boy-boy scenario, leaving only three ways of have the two children: boy-girl, girl-boy, girl-girl. From this, it can be seen only ⅓ of these scenarios would have the other child being also a girl [23] see Boy or girl paradox for more. In general, by using a probability space, one is less likely to miss out on possible scenarios, or to neglect the importance of new information. This technique can be used to provide insights in other situations such as the Monty Hall problem, a game show scenario in which a car is hidden behind one of three doors, and two goats are hidden as booby prizes behind the others. Once the contestant has chosen a door, the host opens one of the remaining doors to reveal a goat, eliminating that door as an option. With only two doors left (one with the car, the other with another goat) the player must decide to either keep their decision, or to switch and select the other door. Intuitively, one might think the player is choosing between two doors with equal probability, and that the opportunity to choose another door makes no difference. However, an analysis of the probability spaces would reveal that the contestant has received new information, and that changing to the other door would increase their chances of winning. [23] See also References ^ The Oxford English Dictionary defines "random" as "Having no definite aim or purpose; not sent or guided in a particular direction; made, done, occurring, etc., without method or conscious choice; haphazard. " a b "Definition of randomness. Retrieved 21 November 2019. ^ The Definitive Glossary of Higher Mathematical Jargon — Arbitrary. Math Vault. 1 August 2019. Retrieved 21 November 2019. ^ Hans Jürgen Prömel (2005. Complete Disorder is Impossible: The Mathematical Work of Walter Deuber. Combinatorics, Probability and Computing. Cambridge University Press. 14: 3–16. doi: 10. 1017/S0963548304006674. ^ Third Workshop on Monte Carlo Methods, Jun Liu, Professor of Statistics, Harvard University ^ Handbook to life in ancient Rome by Lesley Adkins 1998 ISBN   0-19-512332-8 page 279 ^ Religions of the ancient world by Sarah Iles Johnston 2004 ISBN   0-674-01517-7 page 370 ^ Annotated readings in the history of statistics by Herbert Aron David, 2001 ISBN   0-387-98844-0 page 115. Note that the 1866 edition of Venn's book (on Google books) does not include this chapter. ^ Reinert, Knut (2010. Concept: Types of algorithms" PDF. Freie Universität Berlin. Retrieved 20 November 2019. ^ Zeilinger, Anton; Aspelmeyer, Markus; Żukowski, Marek; Brukner, Časlav; Kaltenbaek, Rainer; Paterek, Tomasz; Gröblacher, Simon (April 2007. An experimental test of non-local realism. Nature. 446 (7138) 871–875. arXiv: 0704. 2529. 1038/nature05677. ISSN   1476-4687. PMID   17443179. ^ Each nucleus decays spontaneously, at random, in accordance with the blind workings of chance. Q for Quantum, John Gribbin ^ Longo, Giuseppe; Montévil, Maël; Kauffman, Stuart (1 January 2012. No Entailing Laws, but Enablement in the Evolution of the Biosphere. Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation. GECCO '12. New York, NY, USA: ACM. pp. 1379–1392. arXiv: 1201. 2069. CiteSeerX   10. 1. 701. 3838. 1145/2330784. 2330946. ISBN   9781450311786. ^ Longo, Giuseppe; Montévil, Maël (1 October 2013. Extended criticality, phase spaces and enablement in biology. Chaos, Solitons & Fractals. Emergent Critical Brain Dynamics. 55: 64–79. Bibcode: 2013CSF. 55. 64L. 1016. ^ Breathnach, A. S. (1982. A long-term hypopigmentary effect of thorium-X on freckled skin. British Journal of Dermatology. 106 (1) 19–25. 1111/j. 1365-2133. 1982. tb00897. x. PMID   7059501. The distribution of freckles seems entirely random, and not associated with any other obviously punctuate anatomical or physiological feature of skin. ^ Yongge Wang: Randomness and Complexity. PhD Thesis, 1996. ^ Are the digits of pi random? researcher may hold the key. 23 July 2001. Retrieved 27 July 2012. ^ Laszso Barabasi, 2003) Linked, Rich Gets Richer, P81 ^ Municipal Elections Act (Ontario, Canada) 1996, c. 32, Sched., s. 62 (3. If the recount indicates that two or more candidates who cannot both or all be declared elected to an office have received the same number of votes, the clerk shall choose the successful candidate or candidates by lot. " Reichenbach, Bruce (18 June 1990. The Law of Karma: A Philosophical Study. Springer. p. 121. ISBN   978-1-349-11899-1. ^ Terry Ritter, Randomness tests: a literature survey. ^ Pironio, S. et al. (2010. Random Numbers Certified by Bell's Theorem. 464 (7291) 1021–1024. arXiv: 0911. 3427. 1038/nature09008. PMID   20393558. ^ a b Johnson, George (8 June 2008. Playing the Odds. The New York Times. Further reading Randomness by Deborah J. Bennett. Harvard University Press, 1998. ISBN   0-674-10745-4. Random Measures, 4th ed. by Olav Kallenberg. Academic Press, New York, London; Akademie-Verlag, Berlin, 1986. MR 0854102. The Art of Computer Programming. Vol. 2: Seminumerical Algorithms, 3rd ed. by Donald E. Knuth. Reading, MA: Addison-Wesley, 1997. ISBN   0-201-89684-2. Fooled by Randomness, 2nd ed. by Nassim Nicholas Taleb. Thomson Texere, 2004. ISBN   1-58799-190-X. Exploring Randomness by Gregory Chaitin. Springer-Verlag London, 2001. ISBN   1-85233-417-7. Random by Kenneth Chan includes a "Random Scale" for grading the level of randomness. The Drunkards Walk: How Randomness Rules our Lives by Leonard Mlodinow. Pantheon Books, New York, 2008. ISBN   978-0-375-42404-5. External links Wikiversity has learning resources about Random Look up randomness in Wiktionary, the free dictionary. Wikimedia Commons has media related to Randomness. QuantumLab Quantum random number generator with single photons as interactive experiment. HotBits generates random numbers from radioactive decay. QRBG Quantum Random Bit Generator QRNG Fast Quantum Random Bit Generator Chaitin: Randomness and Mathematical Proof A Pseudorandom Number Sequence Test Program (Public Domain) Dictionary of the History of Ideas: Chance Computing a Glimpse of Randomness Chance versus Randomness, from the Stanford Encyclopedia of Philosophy.

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Use this generator to generate a trully random, cryptographically safe number. It generates random numbers that can be used where unbiased results are critical, such as when shuffling a deck of cards for a poker game or drawing numbers for a lottery, giveaway or sweepstake. How to pick a random number between two numbers? You can use this random number generator to pick a truly random number between any two numbers. For example, to get a random number between 1 and 10, including 10, enter 1 in the first field and 10 in the second, then press "Get Random Number. To generate a random number between 1 and 100, do the same, but with 100 in the second field of the picker. To simulate a dice roll, the range should be 1 to 6 for a standard six-sided dice. To generate more than one unique random number, just select how many you need from the drop-down below. For example, selecting to draw 6 numbers out of the set of 1 to 49 possible would be equivalent to simulating a lottery draw for a game with these parameters. Where are random numbers useful? You might be organizing a charity lottery, a giveaway, a sweepstakes, etc. and you need to draw a winner - this random number generator is for you! It is completely unbiased and outside of your control, so you can assure your crowd of the fairness of the draw, which might not be true if you are using standard methods like rolling a dice. If you need to choose several among the participants instead, just select the number of unique numbers you want generated and you are all set. However, it is usually best to draw the winners one after another, to keep the tension for longer (discarding repeat draws as you go. A random number generator is also useful if you need to decide who goes first in some game or activity, such as board games, sport games and sports competitions. The same is true if you need to decide the participation order for multiple players / participants. Nowadays, a number of government-run and private lotteries and lottery games are using random number generators instead of more traditional drawing methods. RNGs are also used to determine the outcomes of all modern slot machines. Finally, random numbers are also useful in statistics and simulations, where they might be generated from distributions different than the uniform, e. g. a normal distribution, a binomial distribution, a power distribution, pareto distribution. For such use-cases a more sophisticated software is required. Generating a random number There is a philosophical question about what exactly "random" is, but its defining characteristic is surely unpredictability. We cannot talk about the unpredictability of a single number, since that number is just what it is, but we can talk about the unpredictability of a series of numbers (number sequence. If a sequence of numbers is random, then you should not be able to predict the next number in the sequence while knowing any part of the sequence so far. Examples for this are found in rolling a fair dice, spinning a well-balanced roulette wheel, drawing lottery balls from a sphere, and the classic flip of a coin. No matter how many dice rolls, coin flips, roulette spins or lottery draws you observe, you do not improve your chances of guessing the next number in the sequence. For those interested in physics the classic example of random movement is the Browning motion of gas or fluid particles. Given the above and knowing that computers are fully deterministic, meaning that their output is completely determined by their input, one might say that we cannot generate a random number with a computer. However, one will only partially be true, since a dice roll or a coin flip is also deterministic, if you know the state of the system. The randomness in our random number generator comes from physical processes - our server gathers environmental noise from device drivers and other sources into an entropy pool, from which random numbers are created [1. Sources of randomness According to Alzhrani & Aljaedi [2] there are four sources of randomness used in the seeding of the random number generator, two of which are used in our number picker: Entropy from the disk when the drivers call it - gathering seek time of block layer request events. Interrupt events from USB and other device drivers System values such as MAC addresses, serial numbers and Real Time Clock - used only to initialize the input pool, mostly on embedded systems. Entropy from input hardware - mouse and keyboard actions (not used) This puts the RNG we use in compliance with the recommendations of RFC 4086 on randomness required for security [3. True random versus pseudo random number generators A pseudo-random number generator (PRNG) is a finite state machine with an initial value called the seed [4. Upon each request, a transaction function computes the next internal state and an output function produces the actual number based on the state. A PRNG deterministically produces a periodic sequence of values that depends only on the initial seed given. An example would be a linear congruential generator like PM88. Thus, knowing even a short sequence of generated values it is possible to figure out the seed that was used and thus - know the next value. A cryptographic pseudo-random number generator (CPRNG) is a PRNG in that it is predictable if the internal state is known. However, assuming the generator was seeded with sufficient entropy and the algorithms have the needed properties, such generators will not quickly reveal significant amounts of their internal state, meaning that you would need a huge amount of output before you can mount a successful attack on them. A hardware RNG is based on unpredictable physical phenomenon, referred to as "entropy source. Radioactive decay, or more precisely the points in time at which a radioactive source decays is a phenomenon as close to randomness as we know, while decaying particles are easy to detect. Another example is heat variation - some Intel CPUs have a detector for thermal noise in the silicon of the chip that outputs random numbers. Hardware RNGs are, however, often biased and, more importantly, limited in their capacity to generate sufficient entropy in practical spans of time, due to the low variability of the natural phenomenon sampled. Thus, another type of RNG is needed for practical applications: a true random number generator. In it cascades of hardware RNG (entropy harvester) are used to periodically reseed a PRNG. When the entropy is sufficient, it behaves as a true random number generator (TRNG. References [1] Linux manual page on "urandom" 2] Alzhrani K., Aljaedi A. (2015) Windows and Linux Random Number Generation Process: A Comparative Analysis" International Journal of Computer Applications 113:21 [3] Schiller J., Crocker S. (2005) IETF RFC 4086 - Randomness Requirements for Security" 4] Goichon F., Lauradoux C., Salagnac G., Vuillemin T. (2012) A study of entropy transfers in the Linux Random Number Generator" research report 8060.

This form allows you to arrange the items of a list in random order. The randomness comes from atmospheric noise, which for many purposes is better than the pseudo-random number algorithms typically used in computer programs. Part 1: Enter List Items Enter your items in the field below, each on a separate line. Items can be numbers, names, email addresses, etc. A maximum of 10, 000 items are allowed. Please don't enter anything you would consider confidential ( here's why. (you're viewing this form securely) Part 2: Go! Be patient! It may take a little while to randomize your list...