Q: Is normal distribution bimodal

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No, the normal distribution is strictly unimodal.

By specifying the centre and standard deviation of the distribution but also mentioning the fact that it is bimodal and identifying the modes.

This could be a bimodal. There are many other factors that would have to be taken into account as well.

A bimodal distribution.

Nothing. You simply have a distribution that is bimodal. You report both modes.

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No, the normal distribution is strictly unimodal.

Bimodal Distribution

By specifying the centre and standard deviation of the distribution but also mentioning the fact that it is bimodal and identifying the modes.

The distribution is bimodal. That is all there is to it.

No. A distribution may be non-skewed and bimodal or skewed and bimodal. Bimodal means that the distribution has two modes, or two local maxima on the curve. Visually, one can see two peaks on the distribution curve. Mixture problems (combination of two random variables with different modes) can produce bimodal curves. See: http://en.wikipedia.org/wiki/Bimodal_distribution A distribution is skewed when the mean and median are different values. A distribution is negatively skewed when the mean is less than the median and positively skewed if the mean is greater than the median. See: http://en.wikipedia.org/wiki/Skewness

This could be a bimodal. There are many other factors that would have to be taken into account as well.

A bimodality is a bimodal condition - a distribution which has two modes.

A bimodal distribution.

A distribution with 2 modes is said to be bimodal.

Nothing. You simply have a distribution that is bimodal. You report both modes.

In statistics, a distribution curve that has two peaks is referred to as bimodal.

You are likely familiar with the probability density function of the normal distribution--that is, the bell-shaped curve.A bimodal distribution is one whose probability density function has two 'humps' or maxima. In other words, values of the random variable are more likely to occur around where those two maxima occur than elsewhere, in the same way that values of a normally distributed random variable are more likely to occur around its maximum.