Z-Scores in Statistics Explained: Formula, Overview

Z-Scores in Statistics Explained: Formula, Overview
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  • 0:00 Introduction to Z-scores
  • 0:49 Calculating the Z-Score
  • 1:21 Negative Z-Scores
  • 1:36 The Formula for the Z-Score
  • 1:56 Using Z-Scores
  • 2:57 Lesson Summary
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Lesson Transcript
Instructor: Orin Davis
The z-score is the number of standard deviations away from the mean. It allows for standardizing scores so that they can be compared across samples or tests.

Introduction to Z-Scores

Chris took the SATs and got a 2100 out of 2400. Pat took the ACTs and got a 32 out of 36. Who has the higher score?

The SATs and ACTs are measured on entirely different scales, so it's impossible to tell from the raw scores who did better. But, thanks to statistics, with just a little bit of knowledge about the distribution of test scores across the population, we can standardize Chris and Pat's scores by putting them on the same scale. If we know the mean and standard deviation of both tests, we can compare the two scores with respect to how many standard deviations they are above or below the mean. We call this the z-score, the number of standard deviations a given score is from the mean.

Calculating The Z-Score

Let's say the SATs have a mean of 1500, and a standard deviation of 200. That means Chris's score is 600 points higher than the mean, or 3 standard deviations above the mean. Thus, Chris's z-score on the SATs is 3.

Let's say the average ACT score is 24, and the standard deviation is 4. That means Pat's score is 8 points above the mean, or 2 standard deviations above the mean. Thus, Pat's z-score on the ACT is 2.

Negative Z-Scores

What if Jean had scored a 20 on the ACT? In that case, Jean's score would be 4 points below the mean, or 1 standard deviation below the mean. Thus, Jean's z-score on the ACT would be -1.

The Formula for the Z-Score

In all three cases, the z-score was found by subtracting the mean from the raw score and then dividing by the standard deviation.

This is read as x minus mu, divided by sigma, where:

x = sample score
mu = population mean
sigma = population standard deviation

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