Today I saw a Reel talking about the Central Limit Theorem.
In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution.
My project is a simple visualizer of this theory. A line graph is drawn, and several points are placed at the center. For every tick, each point can either move left, right, or stay on the line graph. After several ticks, a curve is drawn. You’ll notice that every time you run the project, the curve looks almost the same. The data is very predictable. Even though the points move at random, you still get almost the same result every time.
Common applications of the Central Limit Theorem include surveys, risk management, quality control predictions, and much more.
If you click the stage before running the project or after the simulation runs, you can change some of the parameters.
Axis range: Length of the line graph.- Example: a value of 10 makes the line graph 21 spaces long, -10 to 10
Movements per trial: How many times each point can move left, right, or stay.- Example: a value of 4 could make a point randomly move
left, left, right, stay, orright, left, right, left, orstay, stay, stay, left, etc.
- Example: a value of 4 could make a point randomly move
Sample size: How many points are placed on the graph.- Example: a value of 40 would place 40 points on the graph.