And, in the case of four equal parts, each part is a quartile. Normally, large data sets are divided into different quantiles for statistical analysis. From a practical perspective, economists use statistical tools, such as quintiles to understand and study data sets. Examples of data study can be the population of a country or a study of demographics and economic status of the population.
Many a time, the government carries out the study to enable implementation of welfare schemes. In other cases, quintiles find use in analysing movements in prices of stocks or commodities in financial markets.
In the analysis, one can find the average price between the second and fourth quintile. The government of a State or an organisation may use various statistical tools to understand the population before implementing any scheme. The government uses quintiles, tertiles, or quartiles to break the data into different segments. The study helps the government in framing suitable social and economic schemes for development or taxation policies.
Products IT. About us Help Center. Log In Where do you want to login? For instance, when a set of data is divided into five equal parts, each of them is called a quintile , which refers both to the cut-off points as well as the group of values contained. When a set of data is divided into ten equal parts, each of them is called a decile. In the field of income statistics, quintiles are used to find out how income is distributed among the population. A quintile is a type of quantile, which is defined as equal-sized segments of a population.
One of the most common metrics in statistical analysis, the median, is actually just the result of dividing a population into two quantiles. A population split into three equal parts is divided into tertiles, while one split into fourths is divided into quartiles. The larger the data set, the easier it is to divide into greater quantiles.
Economists often use quintiles to analyze very large data sets, such as the population of the United States. There would be three quintiles in between the upper and lower quintiles. While the average of all the stock prices typically falls between the second and fourth quintiles, which is the middle point of the data, outliers on either the high end or the low end of the data may increase or decrease the average value.
As a result, it is worth considering the distribution of the data points—and accounting for any significant outliers—when trying to understand the data and the average values. Politicians invoke quintiles to illustrate the need for policy changes. In "The Bell Curve," a controversial book on intelligence quotient IQ , the authors use quintiles throughout the text to illustrate their research, showing that IQ is heavily correlated with positive outcomes in life.
For certain populations, the use of other methods to examine how the data is distributed makes more sense than using quintiles. For smaller data sets, the use of quartiles or tertiles helps prevent the data from being spread too thin. Comparing the mean, or average, of a data set to its median , or the cutoff point where the data is divided into two quantiles, reveals if the data is evenly distributed or if it is skewed toward the top or bottom. A mean that is significantly higher than the median indicates the data is top-heavy, while a lower mean suggests the opposite.
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