Z-Scores (HSC SSCE Mathematics Standard): Revision Notes
Z-Scores
What is a z-score?
A z-score tells us how far a particular score is from the mean, measured in standard deviations. It's a useful way to standardise and compare different scores from a normal distribution.
Z-scores can be:
- Positive when the score is above the mean
- Negative when the score is below the mean
- Zero when the score equals the mean
The beauty of z-scores is that they allow us to compare scores from different datasets by putting them on the same scale. This means we can compare a student's test score to heights of people, or any other normally distributed data, because everything is measured in the same unit: standard deviations from the mean.
The z-score formula
The formula for calculating a z-score is:
Where:
- = the z-score (standardised score)
- = the individual score we're examining
- = the mean of all scores
- = the standard deviation (for population data, use )
This formula works by finding how far the score is from the mean , then dividing by the standard deviation to express this distance in "standard deviation units".
Understanding what z-scores tell us
Interpreting z-score values
The relationship between z-scores and their positions is straightforward:
- A z-score of 0 means the score is exactly at the mean
- A z-score of 1 means the score is one standard deviation above the mean
- A z-score of -1 means the score is one standard deviation below the mean
- A z-score of 2 means the score is two standard deviations above the mean
The larger the z-score (ignoring whether it's positive or negative), the further that score is from the centre of the data. This is crucial for identifying outliers and understanding the spread of your data.
Important property
When you calculate z-scores for all values in a normally distributed dataset, these z-scores will themselves have:
- A mean of 0
- A standard deviation of 1
Calculating z-scores
Let's work through some examples to see how z-scores are calculated in practice.
Worked Example 1: Finding a z-score from given values
Question: For a normal distribution with mean 62 and standard deviation 11, find the z-score for a score of 84 and explain what it means.
Solution:
Write the formula and identify the values:
Where , ,
Substitute into the formula:
Calculate:
Interpretation: The z-score of 2 tells us that 84 is two standard deviations above the mean.
Worked Example 2: Calculating z-score for test marks
Question: Ruby scored 70 on a class test. The class mean was 78 with a standard deviation of 6. What is her z-score? (Answer to one decimal place)
Solution:
Write the formula:
Substitute the values (, , ):
Calculate:
Round to one decimal place:
Answer: Ruby's z-score is , meaning her score was 1.3 standard deviations below the class mean.
Worked Example 3: Multiple z-score calculations
Question: The heights of a group of young women have a mean of 160 cm and a standard deviation of 8 cm. Determine the z-scores for women who are:
- a) 172 cm tall
- b) 150 cm tall
- c) 160 cm tall
Solution:
Part a) For height = 172 cm:
Part b) For height = 150 cm:
Part c) For height = 160 cm:
Worked Example 4: Special z-score values
Question: What is the z-score for a score that is:
- a) Equal to the mean?
- b) One standard deviation above the mean?
- c) One standard deviation below the mean?
Solution:
Part a) When the score equals the mean:
The score is
Substitute into the formula:
Part b) When the score is one standard deviation above the mean:
The score is
Substitute into the formula:
Part c) When the score is one standard deviation below the mean:
The score is
Substitute into the formula:
Key Points to Remember:
- Z-scores measure how many standard deviations a score is from the mean using the formula
- A z-score of 0 means the score equals the mean, positive z-scores are above the mean, and negative z-scores are below the mean
- The larger the absolute value of a z-score, the further the score is from the centre of the data
- Z-scores allow us to compare scores from different normal distributions on a standardised scale
- A set of z-scores from normally distributed data always has a mean of 0 and a standard deviation of 1