Z = (X - μ) / σ

In this equation:

Z denotes the Z-score

X refers to the value of the data point

μ represents the mean of the distribution

σ stands for the standard deviation of the distribution

Identifying Outliers: The utilization of Z-scores proves instrumental in uncovering potential outliers in a dataset. Data points linked to Z-scores that noticeably deviate from the mean (either greater than +3 or less than -3) could be flagged as outliers, necessitating further scrutiny.

Calculating Percentiles: Z-scores play an active role in the calculation of percentiles. These percentiles delineate the relative standing of a data point within a distribution. With the aid of a Z-score table or calculator, determining the percentile rank of a data point becomes feasible.

Assumption of Normality: Z-scores operate under the presumption that data adheres to a normal distribution. If the underlying distribution deviates from normality, Z-scores might not accurately portray the data's relative position within the distribution.

Influence of Extreme Values: In distributions plagued with extreme values or outliers, the mean and standard deviation could be skewed. This distortion might lead to Z-scores that provide potentially deceptive information.

Mexico's net migration rate represents the difference between the number of individuals entering and leaving the country over a given period.

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