# Difference between revisions of "z-Score"

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A [[z-Score]] is a [[score]] that is the (signed) number of [[standard deviation]]s an observation or [[data|datum]] is ''above'' the [[mean]]. | A [[z-Score]] is a [[score]] that is the (signed) number of [[standard deviation]]s an observation or [[data|datum]] is ''above'' the [[mean]]. | ||

− | * <B>AKA:</B> [[Standard Score]]. | + | * <B>AKA:</B> [[z-Score|Standard Score]]. |

* <B>Context:</B> | * <B>Context:</B> | ||

** It can (typically) be a member of a [[Z-Space]]. | ** It can (typically) be a member of a [[Z-Space]]. |

## Revision as of 20:45, 23 December 2019

A z-Score is a score that is the (signed) number of standard deviations an observation or datum is *above* the mean.

**AKA:**Standard Score.**Context:**- It can (typically) be a member of a Z-Space.

**Example(s):**- ...

**Counter-Example(s):****See:**z-Distribution, Dimensionless Number, Population Mean, Statistical Population, Normalization (Statistics), Normal Distribution, Standard Normal Deviate, Student's t-Statistic, z-Factor, Normalizing Constant.

## References

### 2014

- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Standard_score Retrieved:2014-9-20.
- In statistics, the
**standard score**is the (signed) number of standard deviations an observation or datum is*above*the mean. Thus, a positive standard score indicates a datum above the mean, while a negative standard score indicates a datum below the mean. It is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or**normalizing**(however, "normalizing" can refer to many types of ratios; see normalization (statistics) for more).Standard scores are also called z-values,

*z*-scores, normal scores, and**standardized variables;**the use of "Z" is because the normal distribution is also known as the "Z distribution". They are most frequently used to compare a sample to a standard normal deviate (standard normal distribution, with*μ*= 0 and*σ*= 1), though they can be defined without assumptions of normality.The z-score is

*only*defined if one knows the population parameters; if one only has a sample set, then the analogous computation with sample mean and sample standard deviation yields the Student's t-statistic.The standard score is not the same as the z-factor used in the analysis of high-throughput screening data though the two are often conflated.

- In statistics, the