Two important statistical measures describe the distribution. Skewness is the distribution of data that is asymmetrical, while Kurtosis is the weight of the tails compared to a standard distribution. Understanding these concepts is important for assessing data shape and characteristics, which are crucial in statistical modeling, finance and other fields. Data Science Classes in Pune
 
Skewness is an indicator of whether data are symmetrically distributed. Skewness is zero for a perfectly symmetrical distribution such as the normal distribution. When the skewness of a distribution is positive, it means that the right tail is longer than the left. This indicates more extreme values at the top. In income distributions this is commonly observed, with a few individuals earning significantly more than the majority. Negative skewness, on the other hand means that the left-tail is longer and indicates that the data have more extreme values at the lower end. This can be seen with age-at retirement data. Most people retire at a certain age but some retire earlier.

Kurtosis on the other hands, gives insight into the concentration in the tails of the data. The kurtosis of a normal distribution is three. This value serves as a standard. Leptokurtic distributions have a high kurtosis value and heavy tails, which indicate a greater likelihood of extreme values. Financial market returns are a good example, as they show large fluctuations more often than would be expected by a normal distribution. A distribution with low kurtosis is called platykurtic. It has a flat peak and light tails, which indicates that extreme values occur less often. This is often seen in uniform distributions where the values are evenly distributed.

Understanding the overall shape of data is easier when kurtosis and skewness are interpreted together. Financial analysts, for example, use these measures when assessing risk. A highly skewed distribution or kurtotic distribution can indicate unusual trends or outliers. A highly skewed or kurtotic distribution can indicate a bias within a manufacturing process. In quality control, high kurtosis could mean that defects are occurring sporadically, but with serious consequences. These measures are also used by researchers in the social sciences to check response distributions for surveys and ensure that data is consistent with assumptions of parametric statistical tests.

Skewness and Kurtosis can provide valuable insight into the distribution of data, which helps analysts and researchers to make informed decisions. When data is deviated from normality, it can be transformed or used with alternative statistical methods for accurate interpretations and prediction.