Introduction to Quantitative Science
In popular parlance, randomness refers to the apparent or real absence of predictability or pattern in events. An instance of a random event is something that appears to be random without a known pattern, direction or sequence to it. A random series of occurrences, symbols or specific steps often doesn’t follow an intelligible pattern and therefore has no apparent pattern to it. For example, when you are driving your car down a two lane road at fifty miles per hour, there is no way for you to foresee that you will hit a three-year-old child. This example of a random event is completely unexpected and has absolutely no pattern or direction to it.
The adjective random is usually used in science and mathematics, where it refers to the unpredictability or chaotic behavior of celestial bodies. The Greek word randomly comes from the root ran meaning “to toss” or “recklessly.” According to the discipline of statistics, the best definition of random is “an unpredictable occurrence in which the outcome is unknown.” In computer science, random number generators are used as a technique for creating numbers or sequences that are highly random but have a low probability of occurring randomly.
Randomness was first introduced into the study of statistics in the 18th century by William Bates. His work was groundbreaking in the realm of mathematics and came about as a solution to a long standing problem in probability. According to Bates, we can define probability as a “feasible distribution of outcomes” and “a simplex of states of affairs.” He further explained that “the uniformity principle” which states, “A given set of events may be expected with equal chance”, is a perfect example of a deterministic or consistent model of statistics. William Bates was responsible for establishing the statistical normal curve as the ideal benchmark against which we can compare statistical data.
According to the American Heritage Dictionary, “a random variable is any quantity that may be thought to be unbiased and whose presence or absence makes the experimental results inconclusive or subject to significant criticism.” The word “unbiased” is key in this definition because it is an adjective that is used to indicate a test result that is consistent regardless of the observer’s demographic, cultural, or individual factors. For example, if you have two sets of results, one from a student survey and one from a professional survey, it is still considered to be a random variable because the results are not supposed to be influenced by the specific people involved in the surveys (student respondents are typically the target population for the latter, not the former). This is why it is important to take a strong stance toward the use of statistics in general. While a simpleton may postulate, “P(r) o) n’t say anything that can’t be proved” in order to avoid being proven wrong, in the end, there is no such thing as a free lunch.
In order to provide evidence-based support for their claims of the superiority of random number generators, some proponents of probability sampling take the position that it is impossible for random variables to be completely controlled, which they call “spontaneous luck.” The argument goes like this: if it is not random chance that governs the outcome of a trial, then it must be controlled by some mechanism. Proponents of random number generators assert that this mechanism can be implemented in any environment, and it can be done at any time, as long as the desired outcomes are attainable.
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Critics of randomness argue that while random numbers can provide extremely accurate predictions of likely future outcomes, it is irrelevant whether these outcomes are actually random. By examining the history of dice and gaming, it is shown that randomness has always been an intrinsic part of the game. Thus, while a casino can effectively create a random environment through the implementation of dice and random numbers, it is ultimately up to the players themselves to decide whether they want to gamble in a random manner or gamble honestly with equally random numbers generated by the dice. Further, random numbers are only one means of implementing randomness; another popular method is the use of raffle cards.
While both sides have strong points, both also have their shortcomings. Some argue that it is impossible to completely predict the future based on current knowledge, and therefore, the use of random numbers is irrelevant. Others argue that quantum mechanics and statistics do not conflict with each other, and randomness measures only the probabilities, not the outcomes. Quantum mechanics, they argue, deals with waves and wave particles that cannot be predictable, whereas statistics deal with the outcomes of events that are entirely predictable. Critics of random number generators often point out that quantum mechanics has its own problems of its own, such as the uncertainty principle, which suggests that even with measured amounts of knowledge about the microscopic laws of the universe, there is still great uncertainty as to what the future might hold.
In short, while both arguments are compelling, one is generally correct. The uncertainty principle demonstrates how difficult it is to measure the probability of an event, while the reliance on statistics shows us that randomness can only be used as a guide. So when you roll the dice or draw a card, or spin your roulette wheel, remember that no one knows what the outcome will be, and it is ultimately up to you to determine what is right for you. But remember also that no matter what you do, the outcome of your actions is completely unpredictable, and any attempts to alter the odds are ultimately doomed to fail. Use random sequence generation technology to your advantage, and learn to harness the unpredictable power of the universe.
