One test suite considers 300 zeroes to the right of the decimal as infinitesimally bad and 10-14 in a row as quite bad. Under 0.001 is where definite problems usually occur. Can an RNG be too close to perfection too often? Yes, being too predictable. P values above 0.999 are close to perfection. Step 3: A chi-square is taken on these 18 values with 17 degrees of freedom giving a resulting p value. This equation works with any number of tosses. The equation to obtain the exact percentages for bins b=2 and tosses t=100 is:įor 100-0, the odds are 1 / (2^99) = 1 / (2^(t-1))įor 99-1 previous multiplied by 100 (t) divide by 1įor 98-2 previous multiplied by 99 (t-1) divide by 2įor 97-3 previous multiplied by 98 (t-2) divide by 3įor 51-49 previous multiplied by 52 (t-48) divide by 49įor 50-50 previous multiplied by 51 (t-49) divide by 50, then divide again by 2. Minimal gain occurs above 2000.Įxpected values for 2000 sessions of 100 tosses:ĥ0-50 159.1784748 (Notice that 50-50 occurs a mere 7.96% of the time.) 2000 converges well, so 2000 different samples of 100 tosses are used. Step 2: Choice of number of Sessions: 50 sessions is never sufficient, even with huge sample sizes in the millions. Step 1: Choice of sample size: 100 tosses / bits. 50-50 is not the expected value of the ordered bins, so this test is superior to any frequency test that instead expects 50-50. When coins are flipped 100 times, the expected values are 53.9795 of one and 46.0205 of the other, sometimes more heads, sometimes more tails. This test subsumes any frequency test that expects 50-50 because it is more stringent.ĭefinitions: t= tosses / trials b=bins / urns s=sessions of tosses n=sets of sessionsīecause coin tosses are usually not 50-50, this new test can be utilized with great effectiveness using a pool of 40,000,000 bits. That said, described here is a simple effective new Ordered Frequency Test for bits. We actually don't need too many RNG tests because many "subsume" one another. Ī note about testing a Random Number Generator: The preliminary test for any RNG is the Monobit test used by the NIST which simply counts the number of 1s and 0s. Number combination generator or letter combination generator.ĭo you have more examples of use cases for a combination generator? We would love to hear it.Here is a detailed explanation of how to start.To avoid using Excel to create combinations.Create pairs for sport games from 2 teams.list 1: colleagues with junior skills, list 2: colleagues with senior skills. Create pairs of colleagues based on their skills, e.g.Create random combinations of drinks and food.Split up two teams of people into groups of 2, whereby you want 1 person from each team.Split up your exercises where you have 2 categories, e.g.There are a lot of use cases for a combination generator: What can I use the Combination Generator for? You can also choose how you want to separate the combinations, by newline, comma, pipe, space or line between.ĭo you want new features for the combination maker? Please send us a message via Facebook or Instagram, so we can build this feature for you. When selecting a specific number of combination, it will always be a random combination. It's also possible to generate combinations with 3 items per combination.Īfter entering one or two list of items, you will see the possible number of combinations. You can also create combinations from one list of items which will create pairs or combinations. All combination can be unique, random, sorted by input and/or grouped by one list. In the random pairing generator you can choose if you want to generate a number of random combination or all possible combinations without repetition. Combination Generator or Pair Generator is an online tool to pair and generate all possible (unique) combinations from one or two lists of items or names which can be sorted by group, random or by input.
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