75th Percentile Explained

[Favio Cassidy]

25th Percentile - Also known as the first, or lower, quartile. The 25th percentile is the value at which 25% of the answers lie below that value, and 75% of the answers lie above that value.

50th Percentile - Also known as the Median. The median cuts the data set in half. Half of the answers lie below the median and half lie above the median.

75th Percentile - Also known as the third, or upper, quartile. The 75th percentile is the value at which 25% of the answers lie above that value and 75% of the answers lie below that value. Above the 75th or below the 25th percentile - If your data falls above the 75th percentile or below the 25th percentile we still display your data and include a indicator noting that your club's position is above or below those points.

A. If your baby is "following the curve" of the growth chart, she's paralleling one of the percentile lines on the chart, and the odds are good that her caloric intake is fine, no matter how much or how little milk she seems to be drinking.

On the other hand, if she is "falling off the curve," she's dipping below two or more percentile lines on the growth chart, and she may have inadequate nutritional intake. This could represent a real problem.

If a child's weight is at the 50th percentile line, that means that out of 100 normal children her age, 50 will be bigger than she is and 50 smaller. Similarly, if she is in the 75th percentile, that means that she is bigger than 75 children and smaller than only 25, compared with 100 children her age.

Similarly, a steady increase in weight, while the child's height increases at a much slower rate, indicates she may be putting too much extra meat on her bones. This can be a good thing or an early sign of a risk of obesity.

Put the growth chart into context. No child's growth and development is always so smooth and perfect like the lines of the chart. Kids bounce up and down the growth charts, depending on appetite, feeding issues, illnesses, brief feeding strikes, etc.

Percentile is not the same thing as getting a 60% or a 60 out of 100 on an exam. If it was a really easy exam and everyone aced it, the 60th percentile could be an A. If it was a really challenging exam and everyone flunked, then the 60th percentile could be a C or even an F.

The higher up you go in percentiles, the more likely you are to capture outliers or extremes in salary survey data. Paying the 75th percentile could mean a $150,000 salary, but going up to the 90th percentile could result in a jump to $200,000 if there is a subset of companies paying very high salaries for a certain role.

The short answer is no. Many organizations choose to anchor salaries for different departments or even certain roles to different percentiles, especially if some roles are particularly hard to hire or mission-critical.

With this approach, you run the risk of inadvertently creating pay inequity. To avoid this, you need to create a pay structure (a framework of job levels, salary ranges, and pay zones) that ensures you are paying your people consistently and equitably for repeatable roles.

In this post, we outline 7 steps for quickly building pay bands with benchmark data so you can figure out how to determine salary, how you compare to the rest of the market, or how to introduce more transparent pay practices.

Data aggregation is when multiple values are grouped together to give a single summary value. This is especially useful when you want to extract simple but meaningful values from RUM (Real User Monitoring) data that consists of thousands or millions of measurements.

Individual measurements can be represented by a frequency distribution on a histogram chart. That's a fancy way of describing a bar chart where the X (horizontal) axis shows the value of a measurement and the Y (vertical) axis shows the number of measurements that had that value. Take this chart for example:

The chart above shows clusters of page load times on the X axis. The height of the bars represents how many measurements had page load times that fell within each cluster. We can see that the majority of page load times were between 1 and 2 seconds, with a smaller number of page load times on either side.

Notice how there are many clusters to the right of the chart, but they all have a small number of measurements? This is called the "long tail" and represents the users who are having the slowest experience.

We like to think of medians as the "best case" when it comes to performance data, since it only represents what half of your users will experience. The median is typically a stable measurement, so it's good for seeing long-term trends. However the median will typically not show short-term trends or anomalies.

The 75th percentile is a good balance of representing the vast majority of measurements, and not being impacted by outliers. While not as stable as the median, the 75th percentile is a good choice for seeing medium- to long-term trends. We also think the 75th percentile is the best value to use when setting performance budgets.

The 95th percentile encompasses the experience of almost all of your users, with only the most severe outliers excluded. This makes it perfect for spotting short-term trends and anomalies. However the 95th percentile can be volatile, and may not be suitable for plotting long-term trends.

The average is calculated by adding every measurement together, and then dividing it by the number of measurements. One important and slightly confusing thing about the average measurement is: it doesn't exist!

Averages are not suitable for aggregating most performance data, since it typically does not have an even distribution. With such varying distributions, averages will produce inconsistent values across different metrics. For example on the page load histogram above, the average is roughly the same as the 75th percentile. However on the chart below, the average is closer to the 95th percentile. The average of these two metrics represents two completely different sets of users.

By limiting the available aggregations, SpeedCurve can keep these stories consistent across websites, devices, connections, and countries. Adding more percentiles allows the data to be moulded to fit a predefined story, rather than letting the data tell the story.

Glossary of common web performance metrics you can track with synthetic (SYN) and real user monitoring (RUM). We also show browser support for each metric, as well as whether the metric is supported within a single-page application (SPA).

Percentiles are a statistical measurement to help you interpret data. In a page speed context some users will have a fast experiences and some will wait a long time for your page to load. Percentiles allow you to break down how long different numbers of people are waiting.

In web performance, percentiles show how a user experience compares to other experiences on the same website. If the 75th percentile (p75) of the Largest Contentful Paint metric is 2.29 seconds then that means that 75% of users wait less than 2.29 seconds for the main page content, and 25% wait longer than that.

This annotated histogram explains how this works in more detail. Each bar in the histogram indicates how many visitors had a load time in the range indicated on the x axis. For example, about 100 visitors waited less than 0.3 seconds for the LCP.

However, even if the page loads in about 2.3 seconds for 75% of users that still means that 25% of users may be waiting much longer. This may be a problem, so you'll also want to look at higher percentiles like the 90th percentile (p90). In this case the p90 is 3.44 seconds, which means that 10% of visitors wait at least 3.44 seconds for the page to render.

Looking at even higher percentiles can start being impacted heavily by small individual outliers. For example, the p99 which looks at the slowest 1% of experiences. You be able to identify edge cases where your website loads very slowly, but it could also be affected by users with a very slow connection or by reporting errors.

Google focuses on the 75th percentile when reporting Core Web Vitals metrics. The PageSpeed Insights report below shows a p75 Largest Contentful Paint of 4 seconds, which means 25% of users waited more than 4 seconds for the main page content.

Unlike other metrics like the mean value, percentiles are resistant to outliers. If most users on your site wait 2 seconds for the page to load, but one user waited 100 seconds then looking at the mean value would suggest that your website is really slow. But the percentiles ignore all data above the percentile value. If the p75 is 2 seconds then whether other users wait 3 seconds or 10 seconds doesn't impact the metric value.

The flip side of being resistant to outliers is that some user experiences are ignored when calculating the percentile. If you only look at the 75th percentile you might think your website works well for users, but up to 25% of visitors might still have a poor experience.

In this example 67% of users have a good FCP experience with a rendering time below 1.8 seconds. That means the 67th percentile FCP is 1.8 seconds, or slightly better than the 75th percentile value of 2.2 seconds.

The Interaction to Next Paint (INP) metric measures the 98th percentile of interaction delays for a single visit. In practice that means the longest input delay is reported for visits with less than 50 user interactions.

However what if a user clicks on your page 200 times with a delay of 2 milliseconds and then there is another click with a 500 second delay? In that case the outlier doesn't impact your INP score as the 4 worst experiences will be ignored when calculating the 98th percentile.

While the INP for each single visit is calculated by looking at the 98th percentile, the overall Google Core Web Vitals data then reports the 75th percentile of these collected values across a large number of visits.

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