QUESTION = Why can there never be true randomness?

GENERATION = You can't have truly random events because that would mean there is a set of events that is truly random. We call these events "seed" and "event" because they are the result of something that has a definite order and order is what we are trying to measure. We can't measure something that is truly random, because that would mean there is no real order to it, so there is no way to measure something truly random. It's the same reason why we can't measure something that is truly random because there is no order to it, so there is no way to measure something truly random. So it's possible to measure truly random events, but we can't measure something that is truly random because it is a deterministic event. So we can't measure something truly random, because there is no order to it, so there is no way to measure something truly random. The [Mohs-Shannon Law](_URL_0_) states that there is a way to measure truly random events. The problem is that there is no way to measure truly random events without some kind of measurement that we have, and there is no way to measure truly random events. 

RETRIEVAL #0 = Randomness---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, making it impossible for past outcomes to affect future outcomes. In fact, there is no finite number of trials that can guarantee a success. Section::::Misconceptions and logical fallacies.: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 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. It is fallacious to apply this logic to systems designed to make all outcomes equally likely, such as shuffled cards, dice, and roulette wheels. Section::::Misconceptions and logical fallacies.:Odds are never dynamic. 

RETRIEVAL #1 = Statistical randomness---Statistical randomness A numeric sequence is said to be statistically random when it contains no recognizable patterns or regularities; sequences such as the results of an ideal dice roll or the digits of π exhibit statistical randomness. Statistical randomness does not necessarily imply "true" randomness, i.e., objective unpredictability. Pseudorandomness is sufficient for many uses, such as statistics, hence the name "statistical" randomness. "Global randomness" and "local randomness" are different. Most philosophical conceptions of randomness are global—because they are based on the idea that "in the long run" a sequence looks truly random, even if certain sub-sequences would "not" look random. In a "truly" random sequence of numbers of sufficient length, for example, it is probable there would be long sequences of nothing but repeating numbers, though on the whole the sequence might be random. "Local" randomness refers to the idea that there can be minimum sequence lengths in which random distributions are approximated. Long stretches of the same numbers, even those generated by "truly" random processes, would diminish the "local randomness" of a sample (it might only be locally random for sequences of 10,000 numbers; taking sequences of less than 1,000 might not appear random at all, for example). A 

RETRIEVAL #2 = Randomness---algorithms outperform the best deterministic methods. Section::::In science. Many scientific fields are concerned with randomness: BULLET::::- Algorithmic probability BULLET::::- Chaos theory BULLET::::- Cryptography BULLET::::- Game theory BULLET::::- Information theory BULLET::::- Pattern recognition BULLET::::- Probability theory BULLET::::- Quantum mechanics BULLET::::- Statistical mechanics BULLET::::- Statistics Section::::In science.: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. 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. 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 

RETRIEVAL #3 = Randomness---Randomness Randomness is the lack of pattern or predictability in events. A random sequence of events, symbols or steps has no order and does not follow an intelligible pattern or combination. Individual random events are by definition unpredictable, but in many cases the frequency of different outcomes over a large number of events (or "trials") is predictable. 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 haphazardness, and applies to concepts of chance, probability, and information entropy. According to Ramsey theory ideal randomness is impossible especially for large structures, for instance professor Theodore Motzkin pointed out that "while disorder is more probable in general, complete disorder is impossible". Misunderstanding of this leads to numerous conspiracy theories. 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 

RETRIEVAL #4 = History of randomness---government of Myanmar reportedly shaped 20th century economic policy based on fortune telling and planned the move of the capital of the country based on the advice of astrologers. White House Chief of Staff Donald Regan criticized the involvement of astrologer Joan Quigley in decisions made during Ronald Reagan's presidency in the 1980s. Quigley claims to have been the White House astrologer for seven years. During the 20th century, limits in dealing with randomness were better understood. The best-known example of both theoretical and operational limits on predictability is weather forecasting, simply because models have been used in the field since the 1950s. Predictions of weather and climate are necessarily uncertain. Observations of weather and climate are uncertain and incomplete, and the models into which the data are fed are uncertain. In 1961, Edward Lorenz noticed that a very small change to the initial data submitted to a computer program for weather simulation could result in a completely different weather scenario. This later became known as the butterfly effect, often paraphrased as the question: ""Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?"". A key example of serious practical limits on predictability is in geology, where the ability to predict earthquakes either on an individual or on a statistical basis remains a remote prospect. In the late 1970s and early 1980s, computer 

RETRIEVAL #5 = Randomness---beginning of a scenario, one might calculate the probability of a certain event. The fact is, as soon as one gains more information about that situation, they may need to re-calculate the probability. Say we are told that a woman has two children. If we ask whether either of them is a girl, and are told yes, what is the probability that the other child is also a girl? Considering this new child independently, one might expect the probability that the other child is female is ½ (50%). But by building a probability space (illustrating all possible outcomes), we see that the probability is actually only ⅓ (33%). This is because the possibility space illustrates 4 ways of having these two children: boy-boy, girl-boy, boy-girl, and girl-girl. But we were given more information. Once we are told that one of the children is a female, we use this new information to eliminate the boy-boy scenario. Thus the probability space reveals that there are still 3 ways to have two children where one is a female: boy-girl, girl-boy, girl-girl. Only ⅓ of these scenarios would have the other child also be a girl. Using a probability space, we are less likely to miss one of the possible scenarios, or to neglect the importance of new information. For further information, see Boy or girl 

RETRIEVAL #6 = Randomness tests---Randomness tests Randomness tests (or tests for randomness), in data evaluation, are used to analyze the distribution of a set of data to see if it can be described as random (patternless). In stochastic modeling, as in some computer simulations, the hoped-for randomness of potential input data can be verified, by a formal test for randomness, to show that the data are valid for use in simulation runs. In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1..." and rarely going above 4). If a selected set of data fails the tests, then parameters can be changed or other randomized data can be used which does pass the tests for randomness. Section::::Background. The issue of randomness is an important philosophical and theoretical question. Tests for randomness can be used to determine whether a data set has a recognisable pattern, which would indicate that the process that generated it is significantly non-random. For the most part, statistical analysis has, in practice, been much more concerned with finding regularities in data as opposed to testing for randomness. However, over the past century, a variety