There are infinitely many sets of parameters that will generate a bell shaped curves - or near approximations.
The Student's t or binomial, for large sample sizes get very close to the Gaussian distribution. There are others, too.
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
I have included two links. A normal random variable is a random variable whose associated probability distribution is the normal probability distribution. By definition, a random variable has to have an associated distribution. The normal distribution (probability density function) is defined by a mathematical formula with a mean and standard deviation as parameters. The normal distribution is ofter called a bell-shaped curve, because of its symmetrical shape. It is not the only symmetrical distribution. The two links should provide more information beyond this simple definition.
It is called a normal distribution.
The t distribution is a probability distribution that is symmetric and bell-shaped, similar to the normal distribution, but has heavier tails. It is used in statistics, particularly for small sample sizes, to estimate population parameters when the population standard deviation is unknown. The t distribution accounts for the additional uncertainty introduced by estimating the standard deviation from the sample. As the sample size increases, the t distribution approaches the normal distribution.
The mean of a standard normal curve is 0. This curve, which is a type of probability distribution known as the standard normal distribution, is symmetric and bell-shaped, centered around the mean. Additionally, the standard deviation of a standard normal curve is 1, which helps define the spread of the data around the mean.
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
True * * * * * No. The Student's t-distribution, for example, is also bell shaped.
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
I have included two links. A normal random variable is a random variable whose associated probability distribution is the normal probability distribution. By definition, a random variable has to have an associated distribution. The normal distribution (probability density function) is defined by a mathematical formula with a mean and standard deviation as parameters. The normal distribution is ofter called a bell-shaped curve, because of its symmetrical shape. It is not the only symmetrical distribution. The two links should provide more information beyond this simple definition.
It is called a normal distribution.
Normal distribution is a perfectly symmetrical bell-shaped normal distribution. The bell curve is used to find the median, mean and mode of a function.
bell shaped
Gaussian distribution. Some people refer to the normal distribution as a "bell shaped" curve, but this should be avoided, as there are other bell shaped symmetrical curves which are not normal distributions.
The t distribution is a probability distribution that is symmetric and bell-shaped, similar to the normal distribution, but has heavier tails. It is used in statistics, particularly for small sample sizes, to estimate population parameters when the population standard deviation is unknown. The t distribution accounts for the additional uncertainty introduced by estimating the standard deviation from the sample. As the sample size increases, the t distribution approaches the normal distribution.
The mean of a standard normal curve is 0. This curve, which is a type of probability distribution known as the standard normal distribution, is symmetric and bell-shaped, centered around the mean. Additionally, the standard deviation of a standard normal curve is 1, which helps define the spread of the data around the mean.
The distribution of the sample mean is bell-shaped or is a normal distribution.
a Gaussian or 'normal' distribution