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To find the Z-Score of a test score, you need to subtract the mean from the test score and then divide by the standard deviation ($Z = \frac{X - \mu}{\sigma}$).

By calculating Z-Scores for each student, we can compare their performance relative to the mean score; higher Z-Scores indicate better relative performance.

The Z-Score corresponds to a percentile in the normal distribution, allowing us to determine how rare a particular score is within a given dataset.

Calculate the Z-Score for the day's sales and compare it to a threshold, such as 2 or 3, to determine if the sales are statistically unusual.

Z-Scores can standardize test scores from different classes to a common scale, allowing for meaningful comparison.

With a Z-Score, a data point can be classified as an outlier if its value is significantly higher or lower than the mean, typically more than 3 standard deviations away.

Calculating Z-Scores for all data points in a dataset transforms it into a standard normal distribution with a mean of 0 and a standard deviation of 1.

Z-Scores can be used to assess whether products meet quality standards by determining how many standard deviations a measurement is from the process mean.

Set benchmarks based on Z-Scores to award scholarships, so they reflect students' standing relative to their peers.

Z-Scores help analyze weight data by showing how many standard deviations a patient's weight is from the mean weight of the study group.

Z-Scores can measure how much an asset's price deviates from its expected value, providing a way to quantify risk in financial markets.