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
No they are not the same.
The Median is the same as the 50th percentile of a distribution.
yes
The normal distribution.
No they are not the same in a unimodal symmetrical distribution and they will never be
Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point.The Shape of a HistogramA histogram is unimodal if there is one hump, bimodal if there are two humps and multimodal if there are many humps. A nonsymmetric histogram is called skewed if it is not symmetric. If the upper tail is longer than the lower tail then it is positively skewed. If the upper tail is shorter than it is negatively skewed.Unimodal, Symmetric, NonskewedNonsymmetric, Skewed RightBimodal
In math, skewness is a measure of the asymmetry of a probability distribution. A distribution is considered right-skewed if the tail on the right side is longer or fatter than the tail on the left side, and vice versa for left-skewed distributions. Skewness can give insight into the shape of a dataset and how it deviates from a symmetrical distribution like the normal distribution.
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.
You cannot. There are hundreds of different distributions. The shapes of the distributions depend on their parameters so that the same distribution can be symmetric when the parameters have some specific value, but is highly skewed - in either direction - for other values.
The question is how do the mean and median affect the distribution shape. In a normal curve, the mean and median are both in the same point. ( as is the mode) If a distribution is skewed, its tail is either on the right or the left. If a distribution is skewed the median may be a better value to use than the mean since it has less effect on the shape. Also is there are large outliers, the median has less effect and is better to use. So the mean has a bigger effect on the shape many times than the median.
They are two outcomes which are observed the most often, provided they are both observed the same number of times.
No they are not the same.
The Median is the same as the 50th percentile of a distribution.
yes
Yes, they can.Yes, they can. In a symmetric distribution they will be the same.
The normal distribution.
No they are not the same in a unimodal symmetrical distribution and they will never be