Tertile vs. Percentile — What's the Difference?

Tertiles are a method of dividing a dataset into three equal parts based on its values, providing a broad overview of the distribution. Each tertile represents one-third of the data, highlighting the lower, middle, and upper segments of the distribution. This division helps in understanding the spread and general characteristics of the data, useful for identifying trends or disparities within the dataset. On the other hand, percentiles divide the data into 100 equal parts, offering a much more detailed view. Each percentile indicates a value below which a certain percent of observations fall, making it especially valuable for detailed comparisons, such as understanding the exact position of an individual score within a large dataset.

While tertiles give a more generalized understanding by dividing the data into broad segments, percentiles allow for precise positioning within the dataset. The tertile method simplifies data analysis by focusing on three major divisions, which can be particularly helpful in quickly identifying distribution characteristics without needing overly detailed data analysis. Whereas percentiles, by splitting the data into 100 parts, are essential for detailed statistical analysis, such as in educational assessments or competitive exams where understanding the exact rank is crucial.

Tertiles are typically used in fields or scenarios where a broad categorization is sufficient for analysis or decision-making. They simplify complex data sets by highlighting basic distribution trends, making it easier to communicate findings to a non-technical audience. In contrast, percentiles are preferred in contexts requiring high precision, such as academic assessments or in-depth research studies, where understanding the fine nuances of data distribution is necessary.

The calculation and interpretation of tertiles can be straightforward, making them accessible for quick assessments and general analysis. This approach is beneficial for initial stages of data exploration or when the dataset is not too complex. Percentiles, however, require more detailed calculations and interpretations, offering nuanced insights into the data's behavior, particularly useful for identifying outliers or for detailed performance evaluations.

In practical application, tertiles might be used in economic studies to categorize income levels into low, middle, and high, providing a clear but broad picture of income distribution. Percentiles, however, are often used in educational settings, such as determining a student's performance relative to peers, offering a precise measurement of individual achievement.