Top Answer

A distribution that is NOT normal. Most of the time, it refers to skewed distributions.

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0Because very many variables tend to have the Gaussian distribution. Furthermore, even if the underlying distribution is non-Gaussian, the distribution of the means of repeated samples will be Gaussian. As a result, the Gaussian distributions are also referred to as Normal.

The standard normal distribution is a normal distribution with mean 0 and variance 1.

The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.

le standard normal distribution is a normal distribution who has mean 0 and variance 1

When its probability distribution the standard normal distribution.

No, the normal distribution is strictly unimodal.

Yes. When we refer to the normal distribution, we are referring to a probability distribution. When we specify the equation of a continuous distribution, such as the normal distribution, we refer to the equation as a probability density function.

The domain of the normal distribution is infinite.

The standard normal distribution has a mean of 0 and a standard deviation of 1.

The Normal distribution is, by definition, symmetric. There is no other kind of Normal distribution, so the adjective is not used.

The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.

A mathematical definition of a standard normal distribution is given in the related link. A standard normal distribution is a normal distribution with a mean of 0 and a variance of 1.

No. Normal distribution is a continuous probability.

A normal distribution can have any value for its mean and any positive value for its variance. A standard normal distribution has mean 0 and variance 1.

It means distribution is flater then [than] a normal distribution and if kurtosis is positive[,] then it means that distribution is sharper then [than] a normal distribution. Normal (bell shape) distribution has zero kurtosis.

Use the continuity correction when using the normal distribution to approximate a binomial distribution to take into account the binomial is a discrete distribution and the normal distribution is continuous.

A bell shaped probability distribution curve is NOT necessarily a normal distribution.

Everything that is normal and can can be distributed easily is known as normal distribution time.

The distribution of sample means will not be normal if the number of samples does not reach 30.

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.

The normal distribution is always continuous.

It may be or may not be; however a normal distribution is unimodal.

No. Normal distribution is a special case of distribution.

No, the normal curve is not the meaning of the Normal distribution: it is one way of representing it.

One advantage of using normal distribution is that there are less errors. A disadvantage of normal distribution is that it can not be interpreted to terms of probabilities.

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