---
title: "Z scores"
url: "https://kb.moxo.ai/en/article/z-scores"
language: "en"
last_updated: "2026-01-28T10:17:05.996Z"
---

# Z scores

Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values.

So say we have a bell curve, which represents a set of data, with the mean right in the middle. A z-score tells us how many standard deviations a particular value is from the mean. Positive z-scores indicate values above the mean, while negative z-scores indicate values below the mean.

In the case of MOXO Diagnosis, a z-score of 0 represents the average of the norm population for a specific age and gender, and positive or negative z-scores represent how many standard deviations an individual’s performance score is above or below the mean of their norm group.

![](https://cdn-assets.productfruits.com/35700284-8ea6-4010-bca7-60033b5bb5a2)

The Equation

*Z score = ( x - μ ) / σ*

-   x = the MOXO score (number of clicks)
-   μ = the mean of the norm group (per age and gender)
-   σ = the standard deviation of the norm group (per age and gender)

Example calculation: Participant A:  Male 9 years old

![](https://cdn-assets.productfruits.com/9d469904-c1af-4b80-a7ab-1592f421c2e0)

In the MOXO Diagnosis, a score is considered to reflect difficulty in performance if the individual’s z-score falls more than 1.65 standard deviations below the mean of their age and gender matched norm group.![](https://cdn-assets.productfruits.com/05596d9f-74a8-4cb3-8443-56e416b1955c)

![](https://cdn-assets.productfruits.com/cb1402d4-0859-41d6-bc78-4eb2821134cf)

So with that said, why do we use z-scores in the first place? Well, they allow us to compare different sets of data that may have different means and standard deviations. By standardizing our data with z-scores, we can make meaningful comparisons and draw conclusions across different contexts. This allows us to examine all four MOXO Diagnosis metrics- A,T,I and H on a standardized scale and draw conclusions accordingly.