Standard score
In statistics, a standard score indicates by how many standard deviations an observation or datum is above or below the mean. It is a dimensionless quantity derived 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, as in standardized testing; 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.
Calculation from raw score
The standard score of a raw score x is
where:
- μ is the mean of the population;
- σ is the standard deviation of the population.
The quantity z represents the distance between the raw score and the population mean in units of the standard deviation. z is negative when the raw score is below the mean, positive when above.
A key point is that calculating z requires the population mean and the population standard deviation, not the sample mean or sample deviation. It requires knowing the population parameters, not the statistics of a sample drawn from the population of interest. But knowing the true standard deviation of a population is often unrealistic except in cases such as standardized testing, where the entire population is measured. In cases where it is impossible to measure every member of a population, the standard deviation may be estimated using a random sample.
Applications
The z-score is often used in the z-test in standardized testing – the analog of the Student's t-test for a population whose parameters are known, rather than estimated. As it is very unusual to know the entire population, the t-test is much more widely used.
Also, standard score can be used in the calculation of prediction intervals. A prediction interval [L,U], consisting of a lower endpoint designated L and an upper endpoint designated U, is an interval such that a future observation X will lie in the interval with high probability, i.e.
for example γ = 0.95 (95%). For the standard score Z of X it gives:
By determine the quantile z such that
it follows:
Standardizing in mathematical statistics
In mathematical statistics, a random variable X is standardized by subtracting its expected value and dividing the difference by its standard deviation
If the random variable under consideration is the sample mean of a random sample of X:
then the standardized version is
See normalization (statistics) for other forms of normalization.
See also
This article includes a list of references, related reading, or external links, but its sources remain unclear because it lacks inline citations. (May 2012) |
References
Further reading
- Carroll, Susan Rovezzi; Carroll, David J. (2002). Statistics Made Simple for School Leaders (illustrated ed.). Rowman & Littlefield. ISBN 978-0-8108-4322-6. Retrieved 7 June 2009Template:Inconsistent citations
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: CS1 maint: postscript (link) - Richard J. Larsen and Morris L. Marx (2000) An Introduction to Mathematical Statistics and Its Applications, Third Edition, ISBN 0-13-922303-7. p. 282.