Z-Score is a number that can be calculated to help understand where a specific score falls in the grand scheme of the results. Whether or not it is above or below the mean (average) score, and just how far it is from that.
To find the z-score, you must provide the data point (often represented as X, or your score), the mean value, and the standard deviaiton.
The formula looks like this:
Z-Score = X - Mean (μ) Standard deviaiton (σ)
Suppose you have a dataset of exam scores where the mean score is 70 and the standard deviation is 10. A student who scored 85 would have a Z-score of:
Z = 85 - 70 10 = 1.5
This indicates that the student's score is 1.5 standard deviations above the mean.Z-scores are used to standardize data and facilitate comparison across different datasets. They help identify outliers and determine the relative position of a data point within a distribution. Z-scores are particularly useful in fields such as finance, education, and health where comparing data from different sources or populations is common.
Z-scores are particularly useful when dealing with datasets that have different means and standard deviations. They provide a standardized way to compare and interpret data points, regardless of the original scale or distribution of the data.
Create Date: June 13, 2024
Last Modified Date: June 14, 2024
