Standard Normal and z-Tables
Look up probabilities for z.
Key idea
Look up probabilities for z.
In depth
Any normal variable can be standardised as z = (x − μ)/σ, giving mean 0 and SD 1. Tables or software give cumulative probabilities Φ(z), so one table serves every normal distribution.
Worked example
P(X < 85) with μ = 70, σ = 10: z = 1.5, Φ(1.5) ≈ 0.933.
Real-world uses
- Finding the chance a battery lasts longer than a given time
- Reading standard statistics tables
Common mistake
Forgetting that tables usually give the area to the left of z.
How to learn this
- Read the key idea and say it in your own words.
- Work through the worked example, then try a similar problem.
- Use the interactive lesson’s visual and mini-quiz to check the pattern.
Frequently asked questions
What is Standard Normal and z-Tables?
Look up probabilities for z. It is a topic at the advanced level in Probability & Statistics.
Where is Standard Normal and z-Tables used in real life?
Real-world examples: Finding the chance a battery lasts longer than a given time; Reading standard statistics tables.
What should I learn before Standard Normal and z-Tables?
Make sure you are comfortable with Empirical Rule (68–95–99.7), the previous topic in Probability & Statistics.
What is a common mistake with Standard Normal and z-Tables?
Forgetting that tables usually give the area to the left of z.
How can I practise Standard Normal and z-Tables?
Open the interactive lesson, play with the visual, then answer the three-question mini-quiz. Getting two right marks the topic complete.
What comes after Standard Normal and z-Tables?
Move on to Exponential Distribution next.
Related advanced topics in Probability & Statistics
- Quartiles and IQR – Quartiles split data into four parts; IQR = Q3 − Q1.
- Box Plots – Five-number summary visualised.
- Outliers – Values beyond 1.5 IQR from the quartiles.
- Variance – Average squared deviation from the mean.
- Standard Deviation – Square root of variance; typical spread.
- Sample vs Population Variance – Divide by n−1 for sample variance.