z-Score

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

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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.

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