90th Percentile

Definition

The 90th percentile is a statistical measure that indicates the value below which 90% of the observations in a dataset fall. It is a key metric used to understand the distribution and location of data within a given population or sample.

5 Must Know Facts For Your Next Test

1. The 90th percentile is a measure of the upper tail of a data distribution, indicating the value that 90% of the observations fall below.
2. It is commonly used to identify outliers or extreme values in a dataset, as it represents the 10% of observations with the highest values.
3. The 90th percentile is often used in quality control, risk assessment, and decision-making processes to establish thresholds or targets for performance, safety, or regulatory compliance.
4. Calculating the 90th percentile involves arranging the data in ascending order and identifying the value that corresponds to the 90th position in the sorted dataset.
5. The 90th percentile is a robust measure of location that is less affected by outliers than the mean, making it useful for analyzing skewed or heavy-tailed distributions.

Review Questions

• Explain the purpose and significance of the 90th percentile in the context of measures of data location.
  • The 90th percentile is a key measure of the location of data within a distribution, as it represents the value below which 90% of the observations fall. It is particularly useful for identifying outliers or extreme values, and is commonly used in quality control, risk assessment, and decision-making processes to establish thresholds or targets. The 90th percentile is a robust measure that is less affected by outliers than the mean, making it valuable for analyzing skewed or heavy-tailed distributions.
• Describe how the 90th percentile relates to other measures of data location, such as the median and quartiles.
  • The 90th percentile is related to other measures of data location, such as the median and quartiles, as it represents a specific point in the distribution of the data. While the median divides the data into two equal halves, the quartiles (including the 3rd quartile, or 75th percentile) divide the data into four equal parts. The 90th percentile, on the other hand, represents the value below which 90% of the observations fall, providing information about the upper tail of the data distribution. Understanding the relationships between these different measures of location can help analysts gain a more comprehensive understanding of the underlying data.
• Evaluate the advantages and limitations of using the 90th percentile as a measure of data location compared to other statistical measures.
  • The 90th percentile has several advantages as a measure of data location. It is less affected by outliers than the mean, making it a more robust measure for skewed or heavy-tailed distributions. Additionally, the 90th percentile provides valuable information about the upper tail of the data, which can be particularly useful for risk assessment, quality control, and decision-making. However, the 90th percentile also has limitations. It does not provide information about the entire distribution, as the median and quartiles do, and it may not be as sensitive to changes in the central tendency of the data as the mean. Ultimately, the choice of which measure of data location to use will depend on the specific goals of the analysis and the characteristics of the underlying data.

Related terms

Percentile: A percentile is a measure that indicates the relative standing of a value within a dataset, showing the percentage of observations that fall below that value.

Median: The median is the middle value in a sorted dataset, dividing the data into two equal halves.

Quartile: Quartiles are the three values that divide a dataset into four equal parts, with the 1st, 2nd (median), and 3rd quartiles representing the 25th, 50th, and 75th percentiles respectively.