If a distribution is symmetrical, each side of the distribution is a mirror image of the other. For a symmetrical, bell-shaped distribution (known as the normal distribution), the mean, median, and mode of the distribution are equal. The normal distribution can be completely described by its mean and variance.
A distribution is skewed if one of its tails is longer than the other (that is, it is not symmetrical). A symmetrical distribution has no skewness, (the skewness is zero). Skewness refers to the degree of asymmetry of a distribution. It occurs due to the existence of extremely large or small values in the data set. It allows us to see if large positive or negative deviations dominate.
A positively skewed distribution means that it has a long tail in the positive direction (a long right tail). It is sometimes called "skewed to the right." This type of distribution is characterized by many small losses and a few extreme gains.
For a positively skewed distribution, the mode is less than the median, which is less than the mean.
Recall that the mean is affected by outliers. In a positively skewed distribution, there are large positive outliers which will tend to "pull" the mean upward. An example of a positively skewed distribution is that of housing prices. Suppose that you live in a neighborhood with 100 homes. Ninety-nine of those homes sell for $100,000 and there is one house that sells for $1,000,000. The median and the mode will be $100,000, but the mean will be $109,000. The mean has been "pulled" upward by the existence of one distinctive home in the neighborhood.
A negatively skewed distribution has a long tail in the negative direction (a long left tail). It is sometimes called "skewed to the left." It is characterized by many small gains and a few extreme losses.
For the negatively skewed distribution, the mean is less than the median, which is less than the mode. In this case, there are large negative outliers which tend to "pull" the mean downward.
Distributions with positive skew are more common than distributions with negative skew. One example is the distribution of income. Most people make under $40,000 a year, but some make quite a bit more, with a small number making many millions of dollars per year. The positive tail therefore extends out quite a long way, whereas the negative tail stops at zero.
In a more psychological example, a distribution with a positive skew typically results if the time it takes to make a response is measured. The longest response times are usually much longer than typical response times, whereas the shortest response times are seldom much less than typical response times.
Negatively skewed distributions do occur, however.
Tips on how to remember these relative locations:
Typical exam question
There is a certain probability distribution with the characteristics described below:
What type of distribution is this?
When a distribution is normal, the dispersion to the left of the mean is the same as the dispersion to the right of the mean. The highest number above (200) is 100 units larger than the mean, whereas the lowest number (20) is 80 units below the mean. Thus, the distribution is not symmetrical. It is skewed to the right.
| hedgefrog: typical exam question: i'm not sure it's correct. Take sample: 20, 60, 110, 110, 200. Mean=100. Median=mode=110. Negative skew.|
another sample: 20 90 90 200. mean=100. median=mode=90. positive skew.
|TonLoc: hedgefrog, your math is wrong|
| ccharles: Trick to remember: Put the words Mean, Median and Mode in alphabetical order|
If Mean < Median < Mode, < points to the left so it is left / negatively skewed.
If Mean > Median > Mode, > points to the right so it is right / positively skewed.
|rhardin: Hedgefrog's math is right.|
| tschorsch: you cannot assume from a single simple 5 value example that this is the way it works with larger distributions.|
with a skewed to the right distribution approximately normal distribution, usually, max-mean > mean-min.
you have a tail with a very small percentage of the values still pulling the mean in the direction of the tail. but the outliers in the tail can be far greater than the opposite outliers.
if you even look at the 5 value sample, there are two lower outliers, 20 and a marginal outlier 60 where you have only one upper outlier.
with a small sample size, you can usually create something odd that does not correspond with data sets of significant size that are more bell shaped.
| AUAU: Normal Distribtn : mean = median = mode ;|
skewness = 0;
discribed by mean & variance.
+ve skewness: mod<med<mean [+ve(right) tail]
| thekid: Can someone please explain....|
How the 'mode' is equal to the mean and median in a normal distribution?
| Gooner7: @thekid dont worry about all that. Think about it as follows:|
On a test, the median score was 70%, but the average was 65%. That indicates that the distribution was to the negative, negative skew.
|hiyujie: Ccharles, good summary|
|moon13: Thanks Ccharles!!|
|bidisha: yea good tip ccharles|
|chipster: Have to say the explanation here of the whole concept was superior to that in the CFA reference material!! Good work AN.|
|cfastudypl: Thanks AN for the simplification of the concepts!|