# Is the Mean Really Useless?

DevFeed: [Is the Mean Really Useless?](<https://devfeed.tech/articles/is-the-mean-really-useless-12480.md>)

Original publisher: [Read original article](<http://brooker.co.za/blog/2017/12/28/mean.html>)

Author: Marc Brooker

Published: 2017-12-28T00:00:00Z

Content type: opinion

Language: en

Sources: [Marc Brooker's Blog](<https://devfeed.tech/sources/marc-brooker-s-blog.md>), [Marc Brooker's Blog](<https://devfeed.tech/sources/marc-brooker-s-blog-2.md>)

Topics: [Statistics](<https://devfeed.tech/topics/statistics.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [systems](<https://devfeed.tech/topics/systems.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Time Series](<https://devfeed.tech/topics/time-series.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [cpu](<https://devfeed.tech/tags/cpu.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [series](<https://devfeed.tech/tags/series.md>), [statistics](<https://devfeed.tech/tags/statistics.md>), [systems](<https://devfeed.tech/tags/systems.md>), [time](<https://devfeed.tech/tags/time.md>)

## AI overview

The article argues that the mean is not inherently useless, but can obscure outliers and misrepresent system behavior. It explains that all descriptive statistics are incomplete summaries and shows how sampling timing can distort monitoring results, recommending careful interpretation and visualization of time-series data.

## Source excerpt

Is the Mean Really Useless? Don't be too mean to the mean. "The mean is useless" is a commonly-repeated statement in the systems observation and monitoring world. As people correctly point out, the mean (or average1) tends to hide information about outliers, tends to be optimistic for many metrics, and can even be wildly misleading in presence of large outliers. That doesn't mean that the average is useless, just that you need to be careful of how you interpret it, and what you use it for. All descriptive statistics are misleading All descriptive statistics are misleading, and potentially dangerous. The most prosaic reason for that is that they are summaries: by their nature they don't capture the entire reality of the data they are summarizing. There is no way for a single number to capture everything you need to know about a large set of numbers. Anscombe's quartet is the most famous illustration of this problem: four data sets that have very different graphs, but the same mean and variance in 𝑥 and 𝑦, and the same linear trend. Thanks to Albert Cairo and Autodesk, there's an even more fun example: the datasaurus dozen2. There are other, more subtle, reasons that descriptive statistics are misleading too. One is that statistics in real-world computer systems change with time, and you can get very different results depending on how those changes in time align with when you sample and how long you average for. Point-in-time sampling can lead to completely missing some detail, especially when the sampling time is aligned to wall-clock time with no jitter. In this example, we've got a machine that runs a periodic job (like a cron job) every minute, and it uses all the CPU on the box for a second. If we sample periodically, aligned to the minute boundary, we'll think the box has 100% CPU usage. Instead, if we sample periodically aligned to any other second, we'll think it completely idle. If, instead, we sample every second and emit a per-minute summary we'll get a mea