In statistics, the degree of data dispersion can be described by various methods. Among them, a deviation table can effectively reflect the degree of data dispersion. In a deviation table, negative deviation refers to the proportion of data that deviates from the mean by a negative amount. In other words, if the proportion of negative deviation is greater, the more the data is skewed below the mean.
What is the meaning of negative deviation and how does it affect data analysis?
The meaning of negative deviation is that the data tends to deviate below the mean. This indicates that the data tends to be more densely distributed at smaller values. In data analysis, negative deviation can help us understand if there is a specific trend or tendency in the data. Also, if the data distribution is normal, negative skewness has a direct impact on the symmetry and kurtosis of the data. We can perform more in-depth data analysis and modeling based on the information from negative skew.
Dealing with negatively skewed data needs to be done on a case-by-case basis. If the model we use assumes that the data distribution is normal, then we can deal with negatively skewed data by transforming the data or using a model that conforms to the characteristics of a normal distribution; if the data distribution is significantly skewed, then we need to use a combination of analytical methods to increase the stability of the data distribution. In addition, we can also use the truncated tail or weighted methods for negatively skewed data preprocessing, so as to obtain more accurate data analysis results.