You cannot. It has a characteristic bell-shaped curve but so does a Student's t with enough degrees of freedom. There are other distributions which, with suitable choice of parameters can be made to look very similar to the Normal curve.
No, the normal curve is not the meaning of the Normal distribution: it is one way of representing it.
The area under the normal curve is ALWAYS 1.
Your question makes no sense. Significant is a word related to tests. The normal curve is a distribution, not a test.
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.
IQ Test
If the underlying distribution of the product is normally distributed then (and only then) the normal distribution can be used to identify specimens that are outside the acceptable range.
The standard normal curve is symmetrical.
No, the normal curve is not the meaning of the Normal distribution: it is one way of representing it.
It is a normal curve with mean = 0 and variance = 1.
the standard normal curve 2
The area under the standard normal curve is 1.
There is no such thing as an "ormal curve". And a Normal curve IS symmetrical!
The area under the normal curve is ALWAYS 1.
Answer this question...similarities and differences between normal curve and skewness
Your question makes no sense. Significant is a word related to tests. The normal curve is a distribution, not a test.
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 Gaussian curve is the Normal distributoin curve, the commonest (and most studied) of statistical distributions.