Negatively Skewed Distribution

Negatively Skewed Distribution Definition

Negatively skewed distribution refers to the distribution type where the more values are plotted on the right side of the graph, where the tail of the distribution is longer on the left side and the mean is lower than the median and mode which it might be zero or negative due to the nature of the data as negatively distributed.

Explanation

In this, there is a wide gap in the distributions as the negative side is heavy; for example, the data contains the income distribution the income of the rich class is much higher than the lower and middle class and hence there is a wide gap in the income distribution die to which means will be above average as due to high gap. In a negatively skewed distribution, the left tail is longer in the graph. This shows unfavorable conditions for any nation, which means there is very vast inequality in the distribution of income, which might result in the underdevelopment of the nation at large. Poverty, unemployment, etc. increases. In such a situation, the poor get the poorer, and the richer get richer.

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Negatively Skewed Distribution Examples

As shown in the above example, there is a wide gap in the distribution of the income, and the tail is bent more towards the left side of the plotting area, which reflects the distribution is negatively skewed.

Mean = (Sum of all the Number in the Data) / n

Where n is the number of samples

• =$ 3,000 + 4,000+5,000 + 7,000 + 7,500+8,500 + 23,000 / 7
• = $ 58,000/7
• = $ 8,585 (Appx)

Median Value = (n+1)/2 Value

• = (7 + 1 / 2) Value
• = 4^th Value
• = $ 8,000

Mode = Highest Value = $ 35000

Real-Life Examples of Negatively Skewed Distribution

• In cricket, some players made the score lower than the average, some get out on zero, some players score runs which are very low, and only one or two players makes the highest scores and which might result in the winning of the team, but if we saw the scores player-wise the distribution, it is negatively skewed.
• Another Example is university exams; the exams are the same, but a few scoreless, few score average, and a few scores the high percentage, which shows the data is negatively skewed.
• In the USA, most people belong to the average income group, and very few belong to the high-income group. This shows there is an unequal distribution of income. Hence the data is negatively skewed.
• The human life cycle is also an example of negatively skewed distribution as many live the average life, some live very less, and some live a very high life in terms of age.
• The taxation regime of underdeveloped countries and developing countries also show this type of distribution as most people pay the average or low-income tax, whereas only a few people pay very high-income taxes. This is due to the unequal distribution of income and wealth.

Interpretation

• This shows that there is a wide gap between the earnings.
• It shows the underdevelopment of the economy.
• It reflects the poor population of the country.
• It shows the failure of governmental measures on the distribution of income.
• It shows a fault in governmental policies.
• It reflects the slow growth of the country.
• It reflects the exploitation of labor or the availability of cheap labor. Hence the government needs to take measures to provide rights to the laborers.
• It shows the volatile nature of the market.
• This is a sign of weak domestic currency.
• It reflects the losses to the investors hence discouragement of the investment.

Central Tendency of Negatively Skewed Distribution

Central tendency refers to the mean, median, and mode of the distribution. In the case of the normally skewed data, the mean, median, and mode are equal, which shows the equal distribution of income and wealth and the positive role of the government efforts and the development of the economy.

In a positively skewed distribution, there are favorable conditions for the country as a large population belongs to the same group, and very few populations are different from the crowd. In a positively skewed distribution, mean, median, and mode are positives. The mean is, in this case, is greater than the median and the median greater than the mode.

Whereas in negatively skewed distribution, data is shown as unequal distribution, the central tendency is shown as under:

Mode > Median > Mean.

Median is the middle value, and mode is the highest value. But due to unequal distribution median will be higher than the mean.

Negatively Skewed Distribution in Finance

In finance, skewed distribution is used to evaluate the return on the investment. Negatively skewed data is the sign of lower return on investment; hence the investor finds it risky to invest in the countries where the income is negatively skewed due to long term losses and currency fluctuation in the international market. Those investors who look for short term benefits can invest in negatively skewed distributed countries.

Recommended Articles

This has been a guide to What is Negatively Skewed Distribution & its Definition. Here we discuss its examples along with its interpretation. You can learn more about from the following articles –

• Mean vs. Median
• Log-Normal Distribution
• A Priori Probability
• Skewness