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SAM identifies statistically significant genes by carrying out gene specific [[Student's t-test|t-tests]] and computes a statistic ''d<sub>j</sub>'' for each gene ''j'', which measures the strength of the relationship between gene expression and a response variable.<ref name="R4"/><ref name="R5"/><ref name="R6"/> This analysis uses [[non-parametric statistics]], since the data may not follow a [[normal distribution]]. The response variable describes and groups the data based on experimental conditions. In this method, repeated [[permutations]] of the data are used to determine if the expression of any gene is significant related to the response. The use of permutation-based analysis accounts for correlations in genes and avoids [[wikt:Special:Search/parametric|parametric]] assumptions about the distribution of individual genes. This is an advantage over other techniques (e.g., [[ANOVA]] and [[Bonferroni correction]]), which assume equal variance and/or independence of genes.<ref name="R7"/>
The following equation represents the algorithm used by SAM:
<math> d_{i} = {r_{i} \over s_{i} + s_{o}}; i = 1, 2, ... p </math>
where <math>r_{i}</math> is the linear regression coefficient of gene <math>i</math>, <math>s_{i}</math> is the standard error of <math>r_{i}</math>, and <math>s_{o}</math> is a constant chosen to minimize the coefficient of variation of <math>d_{i}</math>. <math>r_{i}</math> is equal to the expression levels (x) for gene i under y experimental conditions.
==Error correction and quality control==
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