Depending on the shape of the distribution, there exist some predictable relationships between the three measures of central tendency. In a perfectly symmetric distribution, the median and mean will have the exact same value.
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If a symmetric distribution is unimodal, then the mode will also be equal to the median and mean.
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Quite often, however, a distribution will exhibit some degree of skewness. Remember that the mean is a measure of centrality that is strongly affected by the extreme scores in the dataset. For this reason, the mean tends to be 'pulled towards' the tail-end of a skewed distribution.

For continuous data, the following guidelines are likely to hold for skewed distributions:

• In a positively skewed distribution, the mean is larger than the median, and both of these measures are larger than the mode:
  • Mean > median > mode

• In a negatively skewed distribution, the opposite relationship holds; the mean is smaller than the median, and both these measures are smaller than the mode:
  • Mode > median > mean

It should be noted that these guidelines for skewed data tend not to hold for discrete data.