Z-Score (Standard Score) Examples in Statistics
Start with the recap, study the fully worked examples, then use the practice problems to check your understanding of Z-Score (Standard Score).
This page combines explanation, solved examples, and follow-up practice so you can move from recognition to confident problem-solving in Statistics.
Concept Recap
A z-score tells you how many standard deviations a value is from the mean, calculated as . Positive z-scores are above the mean; negative z-scores are below. Z-scores allow comparison of values from different distributions.
Z-scores put everything on the same scale. A z-score of +2 means 'two standard deviations above average' - unusually high. A z-score of -1 means 'one SD below average' - somewhat low but normal.
Read the full concept explanation →How to Use These Examples
- Read the first worked example with the solution open so the structure is clear.
- Try the practice problems before revealing each solution.
- Use the related concepts and background knowledge badges if you feel stuck.
What to Focus On
Core idea: Z-Score (Standard Score) asks how a value or feature behaves inside the full distribution.
Common stuck point: Students often know a procedure related to z-score (standard score) but skip the recognition step: Am I interpreting the whole distribution or a value position inside it, rather than just computing a single summary? That leads to a calculation or graph that looks reasonable but answers a different question.
Sense of Study hint: Ask: Am I interpreting the whole distribution or a value position inside it, rather than just computing a single summary?
Worked Examples
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Try these problems on your own first, then open the solution to compare your method.
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Background Knowledge
These ideas may be useful before you work through the harder examples.