Compares the various grading methods in a normal distribution. Includes: Standard deviations, cummulative precentages, percentile equivalents, Z-scores, T-scores, standard nine, percent in stanine
In statistics, a standard score (also called z-score or normal score) 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.
The standard score is not the same as the Z-Factor used in the analysis of high-throughput screening data, but is sometimes confused with it.
The conversion process is called standardizing.
The standard score is
where
- X is a raw score to be standardized
- σ is the standard deviation of the population
- μ is the mean 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 statistics, 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. For example, a population of people who smoke cigarettes is not fully measured.
When a population is normally distributed, the percentile rank may be determined from the standard score and ubiquitous tables.
Standardizing in mathematical statistics
In mathematical statistics, a random variable X is standardized using the theoretical (population) mean and standard deviation:
where μ = E(X) is the mean and σ² = Var(X) the variance of the probability distribution of X.
If the random variable under consideration is the sample mean:
then the standardized version is
See also
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