What is a quantile?
The first quintile represents the lower fifth (1% to 20%) of the data, because the quintile is a dataset statistic that represents 20% of a particular population. The second quintile represents the second fifth (21% to 40%).
Quantiles are used to create cutoff points for a particular population. Government-sponsored socio-economic studies may use quintiles to determine the maximum wealth a family can own to belong to the lowest quintile of society. This cutoff point can be used as a prerequisite for families to receive special government subsidies aimed at helping underprivileged people in society.
- The quintile represents 20% of a particular population. Therefore, the first quintile represents the lower fifth of the data and the last quintile represents the last or last fifth of the data.
- These are commonly used for large datasets and are often called by politicians and economists to discuss the concept of economic and social justice.
- Depending on the size of the population, alternatives to quintiles include quartiles and tertiles.
Quantiles are a type of quantile and are defined as segments of the same size in the population. The median, one of the most common indicators in statistical analysis, is actually the result of dividing the population into two quantiles. The quintile is one of five values that divides a range of data into five equal parts, each of which is 1/5 (20 percent) of the range. The population divided into three equal parts is divided into quartiles, and the population divided into quarters is divided into quartiles. The larger the dataset, the easier it is to divide it into larger quantiles. Economists often use quintiles to analyze very large datasets, such as the US population.
For example, if you look at all the closing prices of a particular stock last year, the top 20% of those prices represent the top fifth of the data. The bottom 20% of these prices represent the bottom fifth of the data. There are three quintiles between the upper and lower quintiles. Normally, the average of all stock prices is between the second and fourth quintiles, which are the midpoints of the data, but outliers of either the upper or lower bound of the data can increase or decrease the average. there is. As a result, when trying to understand the data and the mean, it is worth considering the distribution of data points and the significant outliers.
Common usage of Quintiles
Politicians call quintiles to explain the need for policy changes. For example, a politician who defends economic justice can divide the population into one-fifths and explain how the top 20% of income earners manage an unreasonably large share of wealth. Conversely, politicians calling for the abolition of progressive tax may use quintiles to argue that the top 20% overload the tax burden.
In the controversial 1994 IQ book, The Bell Curve, the author uses quintiles throughout the text to describe his work, and IQ is a big positive outcome of life. It shows that they are correlated.
For a particular population, it makes more sense to use other methods to find out how the data are distributed than to use quintiles. For small datasets, using quartiles or tertiles can help prevent the data from becoming too thin. Comparing the mean or mean of a dataset to its median, or the cutoff point at which the data is divided into two quantiles, is the data evenly distributed, or is the data biased up or down? I know how. An average that is significantly higher than the median indicates that the data is at the top, and a lower average indicates the opposite.