**Percentiles Meaning**

A percentile is a number that indicates where a data point falls within a dataset and how much of the dataset has a lower value. It provides a valuable tool for understanding and interpreting data, allowing for comparisons, identifying trends, and making informed decisions in various fields.

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Percentiles in statistics are useful tools for comparing values in large populations, as they indicate how one value compares to others in the data set. In various fields, including healthcare, education, and market research, people use them. Hence, it offers details on the distribution of data throughout a range of values, from the smallest to the greatest.

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### Key Takeaways

- Percentiles are a valuable statistical tool that divides data into 100 equal parts, providing insights into the distribution and relative position of values within a dataset. People commonly use them to compare values, assess rankings, and track growth or performance.
- These provide a detailed understanding of data distribution. They offer in-depth analysis compared to quantiles and deciles, which provide a general overview of data distribution.
- Percentiles, quartiles, and deciles have various applications in different fields, including education, healthcare, finance, and market research. Due to their ease of interpretation and ability to simplify decision-making processes, people widely prefer these measures.

**Percentiles Explained**

Percentiles provide information about how the data are distributed over the period from the smallest value to the most significant value. Therefore, the term “percentile” is derived from the fact that the data is divided into 100 equal parts or percentiles. In statistics, percentiles are essential tools that help with a variety of data analysis tasks and offer insightful information about the distribution of data. Moreover, they reveal the relative positions and proportions of values. Thus, splitting the data into 100 equal parts provides a thorough understanding of how the data is distributed throughout a dataset.

Furthermore, percentiles allow for comparisons and rankings based on relative positions. Therefore, this makes it simple to rank and understand each member’s performance within a bigger group. For example, a student in the 90th percentile outperforms 90% of test takers. In financial analysis, evaluating performance in relation to market benchmarks and making well-informed decisions can be facilitated by comparing investment returns to percentiles since they produce statistical analysis that is more trustworthy and accurate.

They are often used in the tracking of development and growth, especially in kids. Hence, these growth charts percentile are essential tools for monitoring a child’s development and identifying any potential growth concerns early on. Hence, this is because they provide an organized way to assess physical attributes, including weight, height, and head circumference. A wide range of users, including academics, analysts, politicians, and the general public, can quickly grasp and utilize data representations made simple by percentiles. Additionally, they provide a consistent framework for comparing data points between datasets or within a dataset itself, which is very helpful in many industries.

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**Formula**

Calculating percentiles is easy. The percentile equation calculates the position of a particular value within a data set relative to the rest of the values. It can be done by using the following formula.

*Percentile = (Number of Values in the dataset below the specified number / Total Number of Values in the dataset) Ã— 100*

### Calculation **Examples**

Let us look at a few examples to understand the concept better.

#### Example #1

A recent study found that whereas the original ChatGPT frequently placed in the bottom 10% of the group, GPT-4 performed well on many of the assessments.

Thus, the most recent iteration of the artificial intelligence chatbot ChatGPT, GPT-4, has new processing capabilities that were not achievable with the previous edition and can pass exams for law school and high school with scores in the 90th percentile.

On March 14, 2023, the GPT-4’s designer OpenAI released the test results data, demonstrating that in addition to interpreting “much more nuanced instructions” more creatively and consistently, the system can also transform image, audio, and video inputs to text.

It achieves a score in the upper 10% of test takers on a simulated bar exam,” OpenAI continued. On the other hand, the GPT-3.5 score was in the lower ten percentile.

Therefore, according to the data, GPT-4 scored 163 out of 88 on the LSAT (Law school Admission Test), which is required for admission to law school in the United States for college applicants. On the LSAT, the previous iteration of ChatGPT received a score of just 149, placing it in the bottom 40%.

Additionally, GPT-4 received a score of 298 out of 400 on the Uniform Bar Exam, which is taken by recently graduated law students and grants them the right to practice law in any jurisdiction in the United States.

#### Example #2

Let’s consider a class of 30 students, and we want to determine the percentage of hyperactive students in the class.

Hence, the process involves:

- Collecting data for all 30 students.
- Dividing the number of hyperactive students by the total number of students.
- Multiplying by 100.

In this case, 10 out of 30 students are identified as hyperactive. The percentage of hyperactive students is calculated by dividing the number of hyperactive students by the total number of students and multiplying by 100. In this case,

*Percentage of Hyperactive Students = (Number of Hyperactive Students / Total Number of Students) * 100*

Percentage of Hyperactive Students = (10 / 30) * 100 = 30%

Approximately 30% of the students are hyperactive.

**Advantages And Disadvantages**

Here are the main advantages and disadvantages of percentiles:

#### Advantages

- They give users a consistent method for comparing data points inside a dataset.
- Percentiles are less sensitive to extreme values (outliers) than some other statistical measures, such as the mean.
- Permit simple communication and interpretation of relative positions.
- Make it easier to recognize extreme values.
- Furthermore, they are helpful for monitoring changes in size and progress over time.
- They give a thorough rundown of the data distribution.
- Moreover, percentiles can be applied to various types of data, whether it’s height, weight, test scores, or financial returns.

#### Disadvantages

- They might only partially depict the distribution of the data.
- These may disregard the exact numbers in favor of relative placements.
- Extreme values disproportionately affect percentiles.
- Percentiles can be impacted by sample makeup and size.
- The accuracy of these calculations relies on the quality and representativeness of the data.
- Hence, it does not provide direct information about the central tendency of a dataset.

**Percentiles vs Percentage**

Here are the main differences between the two:

Feature | Percentile | Percentage |
---|---|---|

Definition | A measure indicating the relative position of a value within a dataset | A proportion or ratio is expressed as a fraction of 100. |

Application | Used in statistics to describe the position of a particular value within a dataset, often represented in percentile charts. | These are used in a variety of contexts to express a proportion out of 100, such as test scores, grades, interest rates, etc. |

Relative position | Indicates the position of a value relative to others in a dataset, giving insights into the distribution. | Expresses a proportion relative to the whole or total, providing a standardized way of comparing parts. |

Examples | The 75th percentile is the value below which 75% of the data falls. | 75% of students scored above the average. |

Uses | Growth charts, standardized testing, finance (e.g., investment returns), data analysis, comparing individual performance to a group | Grades, exam scores, interest rates, proportions, comparing part-to-whole relationships in various fields. |

** Percentiles vs Quartiles vs Deciles**

Here are the main differences between the three:

Feature | Percentile | Quartile | Decile |
---|---|---|---|

Definition | Indicates the relative position of a value within a dataset | Quartiles divide a dataset into four equal parts, each containing 25% of the data | Decile divides a dataset into ten equal parts, each containing 10% of the data. |

Number of parts | It divides the data into 100 equal parts, each representing 1% of the dataset. Common percentiles include the 25th, 50th (median), 75th, and so on. | These divide the data into four equal parts, each representing 25% of the dataset. | They divide the data into ten equal parts, each representing 10% of the dataset. |

Common uses | Growth charts, standardized testing, analyzing data distribution. | Describes the spread of data in statistical analysis box plots. | Analyzes the distribution of data and compares individual values to the rest of the dataset. |

### Frequently Asked Questions (FAQs)

**1. How to do percentiles in Excel?**

It can be done through the percentile function. The PERCENTILE.EXC function provides the k-th percentile of values in a range. It requires an array or range of data (defining relative standing) and a percentile value in the range (k). For example, k is in the 0..1 inclusive range.

**2. Can you average percentiles?**

No, percentiles cannot be averaged directly. Percentiles represent positions within a dataset, and averaging them would not reveal anything significant about the distribution of the data. Finding the average of the real data values is a more acceptable calculation.

**3. How do we find percentiles in a normal distribution?**

Percentiles of the normal distribution can be found based on mean and standard deviations on the data set. It can also be through inverse CDF (cumulative distribution function).

**4. Are percentiles linear?**

Percentiles are relative positions within a dataset; variations in percentiles may not always translate into variations in the underlying values. Percentiles reflect the form and spread of the data distribution, and data distributions might be nonlinear. However, percentiles are not linear.

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