Normal distribution, also known as Gaussian distribution, is a statistical concept that describes a symmetrical bell-shaped curve. It is used to represent a set of data where the majority of the values cluster around the mean, and the rest of the values taper off symmetrically on both sides.
The characteristics of a normal distribution are:
Normal distribution is important because it is a common pattern found in many real-world data sets. It allows us to make predictions and draw conclusions about a population based on a sample, and it also serves as a basis for many statistical tests and models. It is also used in quality control, finance, and other fields.
A normal distribution is any distribution that follows the characteristics of a normal curve. A standard normal distribution is a specific type of normal distribution with a mean of 0 and a standard deviation of 1. This allows for easier comparison and calculation of probabilities and percentiles.
The central limit theorem states that the sampling distribution of the sample means of a population will be approximately normally distributed, regardless of the underlying distribution of the population. This means that as the sample size increases, the distribution of sample means will become more and more normal. This is why normal distribution is often used in statistical inference and hypothesis testing.