This is a demonstration of the normal, or Gaussian, distribution. Change the sliders and restart to try different settings. PRNG: 1 is Scratch's built-in random number generator; 2 is a random number generator that I made ( https://scratch.mit.edu/projects/646796303/ ) Also check out my other projects! https://scratch.mit.edu/users/BallGamer89/projects/
If you roll two 6-sided dice, you have a 1/6 chance of having them equal 7. There's only a 1/36 chance that they'll equal 2. That kind of probability is what this project works with. Settings PRNG: 1 is Scratch's built-in random number generator; 2 is a random number generator that I made (https://scratch.mit.edu/projects/646796303/) numOutcomes: the number of "sides" on the "dice" that are rolled numRepeats: the number of "dice" that are rolled How it works At the start, (numOutcomes * numRepeats) classes are created (each class is an item in a list). All the items start at 0. For each iteration, a certain number (numRepeats) of "dice" are rolled, and each "die" has a certain number of sides (numOutcomes), the lowest number being 0. All the numbers "rolled" are added together. For example, a roll of 3, 0, 5, 1, 1, 0, and 4 is 14. Then, that item in the list of classes is changed by 1. In that example, if item 14 had counted to 23, it would now be up to 24. When each class is shown on the graph, it forms a bell-shaped curve. It shows that with this type of statistics problem, it's more likely to get a value somewhere in the middle of the limits.
