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A bell-shaped curve, also known as a normal distribution or Gaussian distribution, is a symmetrical probability distribution in statistics. It represents a graph where the data clusters around the mean, with the highest frequency in the center, and decreases gradually towards the tails.
Here is the Standard Normal Distribution with percentages for every half of a standard deviation, and cumulative percentages: Example: Your score in a recent test was 0.5 standard deviations above the average, how many people scored lower than you did?
All normal distributions, like the standard normal distribution, are unimodal and symmetrically distributed with a bell-shaped curve. However, a normal distribution can take on any value as its mean and standard deviation.
Normal distributions are also called Gaussian distributions or bell curves because of their shape. Table of contents. Why do normal distributions matter? What are the properties of normal distributions? Empirical rule. Central limit theorem. Formula of the normal curve. What is the standard normal distribution? Other interesting articles.
The normal distribution, also called the Gaussian distribution, is a probability distribution commonly used to model phenomena such as physical characteristics (e.g. height, weight, etc.) and test scores. Due to its shape, it is often referred to as the bell curve: The graph of a normal distribution with mean of 0 0 and standard deviation of 1 1.
Normal distributions come up time and time again in statistics. A normal distribution has some interesting properties: it has a bell shape, the mean and median are equal, and 68% of the data falls within 1 standard deviation.
A normal distribution is sometimes informally called a bell curve. [6] However, many other distributions are bell-shaped (such as the Cauchy, Student's t, and logistic distributions). (For other names, see Naming.)