Percentage vs Percentile - How Percentage is Different from Percentile?

When it comes to analyzing numerical data, two commonly used terms are Percentage and Percentile. Although they may seem similar, they serve different purposes and have distinct meanings.

In this article, we will explore the differences between percentage and percentile in detail, their applications, and how they impact various fields.

What is Percentage?

A percentage is a mathematical value that expresses a number as a fraction of 100. It is used to compare quantities and measure relative proportions. The formula for calculating a percentage is:

Percentage Formula

The formula to calculate percentage is:

$$\text{Percentage} = \left( \frac{\text{Part}}{\text{Whole}} \right) \times 100$$

Key Characteristics of Percentage:

  • Absolute Value: It gives a direct measure of a quantity in relation to 100.
  • Fixed Reference: Always calculated in proportion to a fixed whole.
  • Comparison Tool: Useful for comparing different datasets with a common scale.

Common Applications of Percentage:

  • Academic Scores: Exam results (e.g., scoring 85% in a test).
  • Finance & Banking: Interest rates, discounts, tax rates.
  • Market & Business Analysis: Profit margins, growth rates.
  • Statistics & Research: Demographics, population growth.

What is Percentile?

A percentile is a statistical measure that indicates the relative position of a value within a dataset. It divides data into 100 equal parts, with each percentile representing a rank in the dataset. Percentiles are often used in standardized testing, performance assessments, and research analysis.

Percentile Formula

The formula to calculate percentile is:

$$P = \left( \frac{N_L + 0.5 N_E}{N} \right) \times 100$$

Where:

  • \( P \) = Percentile rank
  • \( N_L \) = Number of values less than the given value
  • \( N_E \) = Number of values equal to the given value
  • \( N \) = Total number of values in the dataset

Common Uses of Percentile

  • Standardized Tests: GRE, SAT, CAT, JEE, etc.
  • Health & Fitness: Growth charts for children, BMI percentiles.
  • Salary Comparisons: Employee salary percentiles in job markets.
  • Performance Analysis: Employee evaluation, sports ranking.
  • Big Data & Research: Statistical analysis and probability studies.

Percentage vs Percentile Examples

Percentage Example

Student Exam Score

If a student scores 75 marks out of 100 in an exam, the percentage is:

\( \text{Percentage} = \left( \frac{\text{75}}{\text{100}} \right) \times 100 = 75\% \)

Discount on a Product

If a product originally costs $200 and has a 20% discount, the discount amount is:

\( \text{Discount} = \left( \frac{20}{100} \right) \times 200 = 40 \)

So, the final price is $200 - $40 = $160.

Percentile Example

Exam Ranking

If a student is in the 90th percentile of an entrance exam, it means they scored higher than 90% of students.

Out of 1000 students, the student's rank is approximately:

\( \text{Rank} = (100 - 90) \times \frac{1000}{100} = 100 \)

This means the student is among the top 100 students.

Children's Growth Chart

If a baby is in the 50th percentile for weight, it means:

  • 50% of babies weigh less than this baby.
  • 50% of babies weigh more than this baby.

Key Differences Between Percentage and Percentile

FeaturePercentagePercentile
DefinitionExpresses a fraction out of 100.Shows the relative rank within a dataset.
Formula(PartWhole)×100\left( \frac{\text{Part}}{\text{Whole}} \right) \times 100P=(NL+0.5NEN)×100P = \left( \frac{N_L + 0.5 N_E}{N} \right) \times 100
Type of MeasureAbsolute value.Relative ranking.
InterpretationMeasures how much out of 100.Shows position compared to others.
ExampleScoring 80% in a test.Being in the 90th percentile of a test means outperforming 90% of students.
Common UseFinance, academics, business.Ranking, statistics, analysis.

Conclusion

Both percentage and percentile are essential mathematical tools with different purposes:

  • Percentage is useful for quantitative comparisons.
  • Percentile helps in relative rankings.

Understanding the difference is crucial in education, finance, data science, and research, ensuring proper interpretation of scores, statistics, and rankings.