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Calculate z-score and percentile ranking from your data
In a normal distribution, 68% of data falls within 1 standard deviation, 95% within 2, and 99.7% within 3.
The normal distribution was discovered by Carl Friedrich Gauss in 1809 while studying astronomical observations.
IQ tests use z-scores with mean 100 and standard deviation 15, making scores easy to compare across populations.
Z-scores are used in credit risk models to predict the likelihood of bankruptcy (Altman Z-Score).
A z-score of 3 or higher occurs in only 0.13% of cases - about 1 in 740 observations.
Z-scores work for any normally distributed data, from test scores to heights to manufacturing tolerances.
Traders use z-scores to identify when stock prices deviate significantly from their historical averages.
Z-scores help compare athletes across different eras by standardizing statistics relative to their contemporaries.
A z-score measures how many standard deviations a data point is from the mean. It allows you to compare values from different normal distributions and determine how unusual or typical a value is within its dataset.