Tercile vs. Quantile — What's the Difference?
By Tayyaba Rehman & Maham Liaqat — Updated on April 29, 2024
Tercile divides data into three equal parts, emphasizing distribution into thirds, while a quantile divides into any specified number of equal parts, focusing on general data segmentation.
Difference Between Tercile and Quantile
Table of Contents
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Key Differences
A tercile is a statistical term used to divide a dataset into three equal parts, each containing one-third of the data points. Whereas, quantiles are more general divisions used to split data into equal-sized subsets, but the number of subsets can vary, such as quartiles or percentiles.
Terciles specifically identify the points that split the data into three equal groups, making it a useful tool for understanding the distribution within a medium-sized dataset. On the other hand, quantiles allow for flexibility in the number of divisions, which is particularly useful for different statistical analyses depending on the dataset size and the required granularity.
The use of terciles can be particularly advantageous when the analysis focuses on the lower, middle, and upper parts of the data, providing a clear and simple summary of distribution trends. In contrast, quantiles are beneficial when detailed stratification of the data is necessary, as they can be customized to create any number of divisions, from quartiles and deciles to percentiles.
In practice, terciles are often used in fields like economics to categorize income levels into low, middle, and high, or in test scoring to categorize performances. Quantiles, on the other hand, find extensive applications across various fields including statistics, finance, and machine learning, where detailed data segmentation is crucial.
Despite their usefulness, terciles provide less detailed segmentation compared to many quantile-based divisions, which may limit their application in complex statistical analysis requiring precise data breakdown. Whereas quantiles, by allowing the number of divisions to be tailored, offer more detailed insights and can be more precisely aligned with the specific needs of an analysis.
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Comparison Chart
Divisions
Always three
Any number (e.g., quartiles, deciles)
Data segments
Each segment represents one-third of the dataset
Variable, depending on the number of quantiles
Flexibility
Fixed number of divisions
Highly flexible in number of divisions
Usage
Simpler, clear categorization into three levels
Detailed stratification for varied analysis
Common uses
Medium-sized datasets, economic tiers
Diverse fields needing precise data breakdown
Compare with Definitions
Tercile
One of three groups formed by two dividing points.
Her score fell into the upper tercile, placing her among the top performers.
Quantile
A tool for detailed data analysis and comparison.
Quantile analysis revealed hidden trends in the customer satisfaction data.
Tercile
A statistical measure to observe the distribution within three broad categories.
The tercile breakdown helps understand the range of responses in the survey.
Quantile
A statistical measure dividing a dataset into any number of equal-sized intervals.
Using quantiles helps identify outliers and patterns in the data.
Tercile
A division of data into three equal parts.
The middle tercile of the test scores showed the most consistent performance.
Quantile
A division based on percentile, decile, quartile, etc.
The 90th quantile of incomes is substantially higher, indicating inequality.
Tercile
A method to categorize a dataset into thirds.
Economists often use terciles to classify income levels.
Quantile
Flexible data segmentation method used in diverse statistical analyses.
She applied quantile regression to predict housing prices.
Tercile
A specific application of the quantile concept, limited to three parts.
The tercile analysis provides a simpler overview compared to more detailed quantile analysis.
Quantile
A general term for dividing data into several equal parts.
Quantiles are essential in financial risk assessment to understand potential losses.
Tercile
Alternative form of tertile.
Quantile
In statistics and probability, quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities, or dividing the observations in a sample in the same way. There is one fewer quantile than the number of groups created.
Quantile
Each of any set of values of a variate which divide a frequency distribution into equal groups, each containing the same fraction of the total population.
Quantile
(statistics) One of the class of values of a variate which divides the members of a batch or sample into equal-sized subgroups of adjacent values or a probability distribution into distributions of equal probability.
Common Curiosities
What specific advantage does using terciles provide?
Terciles simplify the analysis by focusing on the lower, middle, and upper thirds of a dataset, making them useful for straightforward comparisons and understanding broad distribution trends.
What industries commonly use quantiles?
Quantiles are widely used in finance, insurance, economics, and health sciences for risk assessment, performance evaluation, and demographic analysis.
How does one calculate terciles?
Terciles are calculated by determining the points that divide the data into three groups, each with a third of the total number of data points, often using statistical software or percentile calculation methods.
Why might one choose terciles over quartiles or deciles?
One might choose terciles for their simplicity and ease of interpretation when only a broad overview of the data is needed, as opposed to more detailed analysis possible with quartiles or deciles.
Can terciles be considered a type of quantile?
Yes, terciles are a specific type of quantile where the data is divided into three equal-sized groups.
Is there a scenario where terciles might not be the best choice for data analysis?
Terciles might not be suitable for very large datasets or when a detailed segmentation of data is required, as they provide only a broad overview compared to more finely divided quantiles.
How does the flexibility of quantiles benefit statistical analysis?
Quantiles allow analysts to customize the number of divisions based on the dataset and analytical needs, enabling more precise and tailored data examination.
What is the difference between a quantile and a percentile?
Quantile is a general term for any point that divides the data into equal-sized parts, while percentile specifically refers to divisions that separate the data into 100 equal parts.
Can terciles be used in all types of data?
Terciles are best used in ordinal or continuous data where dividing into equal parts makes sense and is meaningful for analysis.
How are quantiles represented graphically?
Quantiles can be represented using box plots, histograms, or cumulative distribution plots, which visually depict how the data is distributed across the chosen divisions.
How do terciles help in economic analysis?
In economics, terciles help categorize data like income or wealth into low, middle, and high groups, aiding in socio-economic studies and policy making.
What mathematical tools are used to calculate quantiles?
Quantiles can be calculated using statistical formulas, software that supports data analysis, or by arranging data in ascending order and applying percentile formulas.
How do quantiles improve machine learning models?
Quantiles in machine learning can help in handling outliers, improving model robustness, and providing a more detailed understanding of the data's distribution, which is critical for predictive accuracy.
Do terciles and quantiles require a large amount of data to be effective?
While both can be applied to any sized dataset, their effectiveness increases with larger datasets which provide more data points for a meaningful division and analysis.
What challenges might one face when using quantiles?
Challenges include determining the appropriate number of divisions, dealing with data with lots of ties, or interpreting results from unevenly distributed datasets.
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Written by
Tayyaba RehmanTayyaba Rehman is a distinguished writer, currently serving as a primary contributor to askdifference.com. As a researcher in semantics and etymology, Tayyaba's passion for the complexity of languages and their distinctions has found a perfect home on the platform. Tayyaba delves into the intricacies of language, distinguishing between commonly confused words and phrases, thereby providing clarity for readers worldwide.
Co-written by
Maham Liaqat
