![]() The score itself can be found by using algebra and solving for x. The standard normal distribution allows us to interpret standardized scores and provides us with one table that we may use, in order to compute areas under the normal curve, for an infinite number of data sets, no matter what the mean or standard deviation.Ī z-score is calculated as z = x − μ σ z = x − μ σ. ![]() ![]() Z-scores can be looked up in a Z-Table of Standard Normal Distribution, in order to find the area under the standard normal curve, between a score and the mean, between two scores, or above or below a score. Likewise, it does not make sense to compare scores from two different samples that have different means and standard deviations. It would not make sense to compare apples and oranges. Z-scores allow for comparison of scores, occurring in different data sets, with different means and standard deviations. A z-score indicates the number of standard deviation a score falls above or below the mean. It represents a distribution of standardized scores, called z-scores, as opposed to raw scores (the actual data values). The standardized normal distribution is a type of normal distribution, with a mean of 0 and standard deviation of 1.
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