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.
a Gaussian or 'normal' distribution
It is not at all skewed. As to oddly shaped, it depends on your expectations.
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.
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.
bell shaped
The distribution of the sample mean is bell-shaped or is a normal distribution.
a Gaussian or 'normal' distribution
It is not at all skewed. As to oddly shaped, it depends on your expectations.
the normal distribution are a very important class of statistical distributions.all normal distributions are symmetric and have bell- shaped density curves with a single peak.both the normal and symmetrical distributions are u-shape and equal from both sides. the normal distribution is considered the most prominent probability distribution in statistics.There are several reasons for this first, the normal distribution is very tractable analytically. that is a large number of results involving this distribution can be derived in explicit from.Second, the normal distribution arises as the outcome of the central limit theorem, which states that under mild conditions the large number of variables is distributed approximately normally.finally, the "bell" shape of the normal distribution marks it is a convenient choice for modeling a large variety of random variables encountered in practices.