Percentile in Statistics: Why You Should Know This in 2025!

A percentile in statistics represents the value below which a given percentage of observations fall. It is a crucial measure for understanding the distribution of data, especially in data science. 

For instance, in a student test score dataset, the 90th percentile indicates the score below which 90% of students fall, helping data scientists assess performance and set benchmarks effectively.

In this blog, you will explore what is percentile in statistics, how it is calculated, and its significance in data analytics.

What is Percentile in Statistics? Everything You Need to Know in 2025

Percentile in statistics ranks data points within a dataset to show their relative position. They tell us what percentage of values fall below a specific point. Further, they help us understand where any value stands compared to all others.

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Percentiles serve several important purposes:

• Relative Standing: They provide a way to understand an individual's or an item's position relative to the rest of the dataset.
• Outlier Detection: They help identify outliers and unusual values that may warrant further investigation.
• Data Division: They create meaningful divisions within data, facilitating more nuanced analysis.
• Standardization: They allow for standardized testing and comparisons across different datasets.
• Statistical Foundation: They form the basis for many statistical methods and visualizations, enhancing data interpretation.

When calculating percentiles, we determine the value that marks the point below which x% of the data falls. This ranking method provides a clearer picture of data distribution than merely examining averages or ranges.

Also Read: Basic Fundamentals of Business Statistics

How is Percentile Different From Quartile and Decile?

There are three statistical divisions to help us segment data in different ways. Percentiles split data into 100 equal parts, quartiles divide it into 4 parts, and deciles separate it into 10 sections. Each measure provides a different level of detail depending on your analysis needs and data size. 

The table below compares the three measures of statistics:

If you want to learn more about statistical analysis, upGrad’s free Basics of Inferential Statistics course can help you. You will learn probability, distributions, and sampling techniques to draw accurate conclusions from random data samples.

Also Read: What is the Difference Between Percentage and Percentile?

Next, let’s look at how you can calculate percentile in statistics.

Calculating Percentile in Statistics: What is the Best Method in 2025?

Calculating percentiles is crucial for understanding data distribution. Modern methods of calculation have evolved to handle large, complex datasets more efficiently. In 2025, the best techniques for calculating percentiles focus on accuracy, speed, and flexibility across varied data types. With advancements in computational tools, you now have methods that can handle both continuous and discrete data, as well as large datasets with ease.

Let's explore the step-by-step process to find percentiles accurately using current approaches:

Step 1: Organize and Sort Your Data

Before calculating any percentiles, we must prepare our data properly. This first step forms the foundation for all percentile calculations:

1. Data Cleaning
Start by checking your data for errors, missing values, and outliers. Data cleaning techniques ensure your percentile calculations reflect the true nature of your information rather than computational mistakes. Inspect your dataset for potential issues and identify and remove duplicate entries

Handle missing values through:

• Removal (if few in number)
• Imputation (replacing with mean, median, or estimated values)

Also, check for and address outliers that might skew your results.

2. Data Sorting
Next, arrange all values in ascending order from smallest to largest. This sorting creates the ranked sequence necessary for percentile calculations. Sorting differs for different types of data: 

• For numerical data, sorting is straightforward
• For categorical data, establish a meaningful numerical representation
• Time-series data requires chronological arrangement before percentile analysis.

Example: Raw test scores: [87, 65, 93, 72, 81] Sorted scores: [65, 72, 81, 87, 93]

For large datasets (common in 2025), use computational tools rather than manual sorting. Statistical software packages and programming languages like Python and R offer efficient sorting algorithms that can handle millions of data points in seconds.

The quality of your percentile calculations depends on how well you execute this sorting step. A single misplaced value can alter your results, so double-check your sorted dataset before proceeding.

Also Read: Steps in Data Preprocessing: What You Need to Know?

Step 2: Apply the Percentile Formula

To find a specific percentile, we need a formula that identifies the exact position within our sorted data. In 2025, statisticians may use various formula variations depending on the data structure and analysis goals.

Basic Percentile Formula:

Where:

• n = number of values in your dataset
• P = the desired percentile (e.g., 21 for the 21st percentile)

Example:

For a dataset with 20 points, the position for the 40th percentile would be:

Position = [(20 + 1) × 40] ÷ 100 = 8.4

This means the 40th percentile lies between the 8th and 9th values in your sorted dataset.

Weighted Percentiles in Data Analysis:
In data analysis, not all data points are created equal. Sometimes, certain observations carry more significance than others due to their inherent importance, reliability, or context. This is where weighted percentiles become important and are useful in scenarios like:

• Economic studies where population subgroups have different representative weights
• Scientific research with varying sample reliability
• Financial analysis with unequal investment contributions
• Quality control, where some measurements are more critical than others

Methodology of Weighted Percentile Calculation:
Here, we modify the approach to use cumulative frequency and weight summation rather than a simple positional formula. A more precise weighted percentile calculation follows these steps:

1. Order data by value and compute cumulative weights using the formula:

Where

represents the total cumulative weight.

• _Wk​ = Individual weights associated with each data point.
• S_n = The cumulative sum of weights up to the nth observation.
• C = A constant used for adjustment (commonly 0.5 for weighted median).
• p_n​ = The position of the nth-weighted percentile within the cumulative frequency distribution

2. Determine where the desired percentile falls within the cumulative distribution.

3. When the exact percentile does not align with a data point, linear interpolation helps:

• Estimate the value between known data points
• Provide a more accurate representation of the weighted distribution.

Step 3: Handle Ties and Interpolation

In statistical analysis, data does not always fall into perfect and predictable patterns. Consider a situation where you are trying to find the 23rd percentile in a dataset. However, the exact position does not come under any specific data point. In situations like these, interpolation plays an important role in data analysis.

Linear interpolation helps find percentile values that fall between observations. It is a mathematical method used to estimate unknown values that lie between two known data points. It employs linear polynomials to create a straight line connecting these points, allowing for the calculation of intermediate values.

The formula is given as:

Percentile value = Lower value + [(Upper value - Lower value) × decimal portion of position]

or

Where:

•  X_L= value at the lower index
• X_H = value at the higher index
• d = decimal fraction of the computed position (if the position is 8.7, then d=0.7)

Example Calculation:

If your position calculation gives 8.7, take the 8th and 9th values in your dataset and find a point that is 70% of the way between them.

Handling Ties:

Several approaches exist for managing ties in your data:

• Empirical Cumulative Distribution Function (ECDF): Determines percentile positions based on cumulative proportions.
• Average Rank Method: Assigns average positions to all tied values.
• First Occurrence Method: Uses the first appearance of tied values.
• Random Tie-Breaking: Randomly ordered tied values (useful in simulations).

For large datasets with many ties, ECDF and frequency-weighted interpolation are effective methods. Specialized algorithms can efficiently handle ties by:

• Using Timsort (Python) and Quicksort Algorithm variants (R and NumPy) for fast sorting.
• Applying streaming percentile estimation algorithms like P² or TDigest to approximate percentiles without requiring full dataset storage.
• Leveraging parallel processing techniques in cloud-based tools for real-time analytics.

Also Read: Top 20+ Data Science Techniques To Learn in 2025

Next, let’s look at how Percentile in Statistics is different from Quartile and Decile.

Common Misconceptions of Percentile in Statistics and How to Address Them?

People often misunderstand percentiles, which leads to incorrect data interpretation and flawed decisions. These misunderstandings stem from confusion about what percentiles truly measure and how they relate to the underlying data distribution.

Below is a table summarizing the common challenges, along with their corresponding solutions:

If you want to know how to visualize data with Tableau, upGrad’s free Introduction to Tableau can help you. You will learn data analytics, transformation, and visualization using various chart types to generate actionable insights. 

Also Read: Power Analysis in Statistics 2025: Comprehensive Guide

Next, let’s look at some common applications of Percentile in Statistics.

Common Use Cases of Percentiles in Statistics

Percentiles now play a central role in numerous fields, helping professionals make sense of complex data. From healthcare to finance, education to technology, percentiles provide a standardized way to compare values within large datasets. They help make informed decisions based on relative positioning.

Here are the common applications of percentiles in statistics in 2025:

1. Educational Testing and Performance Assessment

Percentiles are used in education to compare an individual’s performance against the general population.

Example: Suppose a student’s score on a standardized test is 88, and the scores of 100 students are listed. The student is ranked 85th in the list.

Calculation: To calculate the percentile rank for this student, use the formula:

Percentile Rank=(Number of scores below the student’s score​/Total number of scores)×100

For this example:

• Number of scores below the student = 84 (since the student is 85th)
• Total number of scores = 100

Percentile Rank=(84/​100)×100

Thus, the student's score is in the 84th percentile, meaning they performed better than 84% of the test-takers.

2. Income Distribution and Economic Analysis

Percentiles help analyze the distribution of income or wealth across different segments of the population.

Example: In a city, the 80th percentile of annual income is INR 25L. This means that 80% of the people earn less than INR 25L.

Calculation: To find the 80th percentile income, you need the ordered data of income in ascending order. If the dataset has 100 individuals, the 80th percentile income corresponds to the income value at position 80 in the sorted list.

For simplicity:

• 80th Percentile income = INR 25L.

This indicates that 80% of the population earns less than INR 25L, and 20% earn more.

3. Health and Medical Research

Percentiles are used to assess health data, such as children's growth patterns, and to compare them with population norms.

Example: A child's weight is in the 75th percentile for their age group, which means their weight is higher than 75% of the children of the same age.

Calculation: To find the 75th percentile, you need the ordered dataset of children's weights. If the dataset contains 100 children, the 75th percentile corresponds to the weight at position 75.

For example, if the 75th child in the ordered list has a weight of 25 kg, this means 75% of children have a weight less than 25 kg.

4. Customer Segmentation in Marketing

In marketing, percentiles help identify customer behavior, such as purchase frequency, and tailor marketing strategies.

Example: A company identifies customers based on spending habits. A customer who is in the 90th percentile has spent more than 90% of the customers.

Calculation: For a dataset of 1000 customers, the 90th percentile customer corresponds to the spending amount at position 900 in the ordered list.

• If the 900th customer spent INR 2000, the 90th percentile spending is INR 2000. 
• This means that 90% of the customers spent less than INR 2000, and 10% spent more.

5. Quality Control and Manufacturing

Percentiles are used in manufacturing to measure product quality and consistency, such as the lifespan of products.

Example: In a factory that manufactures light bulbs, the 95th percentile lifespan is 1000 hours. This means 95% of the light bulbs last less than 1000 hours.

Calculation: Suppose you have a dataset of light bulb lifespans ordered from shortest to longest. To find the 95th percentile:

• If the dataset contains 1000 bulbs, the 95th percentile will be the 950th bulb in the ordered list.
• If the 950th bulb has a lifespan of 1000 hours, it means 95% of bulbs last less than 1000 hours, and 5% last longer.

Percentile in Statistics helps to provide clear benchmarks and meaningful comparisons that improve insights and actions based on data.

Future Trends in Percentile Analysis (2025 and Beyond)

Percentile use in statistics continues to advance as data volumes grow and computational methods improve. New approaches now make percentiles more accessible and meaningful across diverse fields. The integration of automated tools, ethical frameworks, and advanced visualization techniques reshapes how we understand and use percentiles in decision-making.

Here are some trends to watch out for:

1. Automation in Percentile Calculations

Automated systems now handle percentile calculations with unprecedented speed and accuracy. These tools process massive datasets in real-time, turning raw numbers into information without human intervention.

Example: In a large financial institution, automated systems calculate percentiles for daily stock price movements, enabling traders to quickly identify unusual market behavior without manual intervention. This speeds up decision-making and allows for more accurate trading strategies.

2. Smart Analytics and Market Responsiveness

Smart analytics platforms now monitor percentile shifts automatically. This automation helps businesses respond quickly to changing market conditions without constant manual analysis

Example: A retail chain uses automated percentile tracking to monitor the performance of products in different regions. When an item falls below the 25th percentile in sales in any region, the system triggers an automatic inventory adjustment to prevent overstocking and improve market responsiveness.

3. Multi-Dimensional Performance Insights

AI-powered dashboards calculate percentiles across multiple dimensions simultaneously. A marketing team can view customer engagement metrics by age group, location, and purchase history all at once. These percentile rankings are updated as new data arrives. This multi-dimensional view helps identify patterns that remain hidden in simpler analyses.

Example: A social media platform uses AI-powered dashboards to calculate percentiles for user engagement, segmenting data by age, region, and content type. This helps the marketing team quickly identify which user groups are underperforming, allowing for targeted content creation strategies.

4. Edge Computing and IoT Integration

Edge computing brings percentile calculations closer to data sources. IoT devices now perform basic percentile calculations before sending data to central servers, reducing bandwidth needs and enabling faster response times. A manufacturing plant can use this approach to monitor equipment performance, with sensors calculating real-time percentile rankings for temperature, vibration, and pressure.

Example: In a smart manufacturing plant, IoT sensors monitor equipment like motors and pumps. The sensors calculate real-time percentiles for operational metrics (e.g., temperature and pressure) and send alerts when these fall below the 10th percentile, prompting immediate maintenance actions.

5. Anomaly Detection

Automated anomaly detection depends on percentile thresholds. Systems continuously monitor data streams, flagging values that fall outside established percentile ranges for review. This approach helps organizations identify potential issues before they become serious problems, from network security threats to quality control failures.

Example: In cybersecurity, automated systems use percentile thresholds to detect unusual network traffic. When data packets exceed the 95th percentile of normal traffic, the system flags potential security breaches, alerting the security team for further investigation.

Also Read: How to Become a Statistician: Key Skills, Tools & Career Guide

Next, let’s look how upGrad can help you learn the more advanced applications of percentile in statistics.

How upGrad Can Help You Learn Statistics?

Industries increasingly rely on data-driven insights, so understanding percentiles and other statistical tools is critical. Percentile in statistics plays a key role in analyzing large datasets and identifying trends across sectors like healthcare, finance, and marketing.

As data volumes grow and analytics become more integrated into daily operations, the importance of percentiles will only increase.

upGrad’s statistics courses provide hands-on experience with real-world projects, equipping you to interpret data effectively and advance your career in data science and analytics.

In addition to the programs covered in the blog above, here are some courses that can help you in your learning journey:

• Introduction to Data Analysis using Excel
• Case Study using Tableau, Python and SQL
• Hypothesis Testing
• Unsupervised Learning: Clustering
• Logistic Regression for Beginners
• Linear Regression - Step by Step Guide

If you're unsure where to begin or which area to focus on, upGrad’s expert career counselors can guide you based on your goals. You can also visit a nearby upGrad offline center to explore course options, get hands-on experience, and speak directly with mentors!

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