Confidence Intervals for Means
Range likely to contain the true mean.
Key idea
Range likely to contain the true mean.
In depth
An interval x̄ ± t*·(s/√n) captures the true mean in 95% (or the chosen level) of repeated samples. It reflects uncertainty from sampling, with width shrinking as n grows.
Worked example
x̄ = 50, s = 10, n = 25, t* ≈ 2.06: 50 ± 4.1 → (45.9, 54.1).
Formula
Real-world uses
- Reporting the likely range of average heights or average response times in a study
Common mistake
Interpreting it as a 95% chance the mean lies in this one interval.
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 Confidence Intervals for Means?
Range likely to contain the true mean. It is a topic at the expert level in Probability & Statistics.
What is the formula for Confidence Intervals for Means?
The key formula is x̄ ± z*·σ/√n.
Where is Confidence Intervals for Means used in real life?
Real-world examples: Reporting the likely range of average heights or average response times in a study.
What should I learn before Confidence Intervals for Means?
Make sure you are comfortable with Standard Error, the previous topic in Probability & Statistics.
What is a common mistake with Confidence Intervals for Means?
Interpreting it as a 95% chance the mean lies in this one interval.
How can I practise Confidence Intervals for Means?
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 Confidence Intervals for Means?
Move on to Confidence Intervals: Proportions next.
Related expert topics in Probability & Statistics
- Skewness and Kurtosis – Measures of asymmetry and tail heaviness.
- Residuals and R² – Error of a fit and proportion of variance explained.
- Simpson’s Paradox – A trend reverses when groups are combined.
- Law of Total Probability – Split an event over a partition.
- Bayes’ Theorem – Update beliefs with evidence.
- Medical Tests and Base Rates – Why positive results can still be probably false.