About 95% of the x values lie between –2 σ and +2 σ of the mean µ (within two standard deviations of the mean). About 68% of the x values lie between –1 σ and +1 σ of the mean µ (within one standard deviation of the mean). The Empirical RuleIf X is a random variable and has a normal distribution with mean µ and standard deviation σ, then the Empirical Rule states the following: This score tells you that x = 10 is _ standard deviations to the _(right or left) of the mean_(What is the mean?). Suppose Jerome scores ten points in a game. between negative infinity and Z).Jerome averages 16 points a game with a standard deviation of four points. This table gives a probability that a statistic is less than Z (i.e. Note that for z = 1, 2, 3, one obtains (after multiplying by 2 to account for the interval) the results f ( z) = 0.6827, 0.9545, 0.9974, If X is a random variable from a normal distribution with mean μ and standard deviation σ, its Z-score may be calculated from X by subtracting μ and dividing by the standard deviation: The standard normal distribution, represented by Z, is the normal distribution having a mean of 0 and a standard deviation of 1. Normal distributions are symmetrical, bell-shaped distributions that are useful in describing real-world data. 
Since probability tables cannot be printed for every normal distribution, as there are an infinite variety of normal distributions, it is common practice to convert a normal to a standard normal (known as a z-score) and then use the standard normal table to find probabilities. It is used to find the probability that a statistic is observed below, above, or between values on the standard normal distribution, and by extension, any normal distribution. In statistics, a standard normal table, also called the unit normal table or Z table, is a mathematical table for the values of Φ, the cumulative distribution function of the normal distribution. Table of probabilities related to the normal distribution
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