Confidence Intervals for Means
Construct and interpret confidence intervals for a population mean using the t-distribution.
Try the Interactive Version!
Learn step-by-step with practice exercises built right in.
📏 Confidence Intervals for Means
When to Use t-Interval vs z-Interval
- Use t-interval: When σ (population SD) is unknown (almost always in practice)
- Use z-interval: Only when σ is known (rare in real applications)
One-Sample t-Interval Formula
where:
- = sample mean
- = critical t-value (depends on confidence level and degrees of freedom)
- = sample standard deviation
- = sample size
Conditions for t-Interval
- Random sample: Data collected randomly
- Independence: Sampling without replacement; use 10% rule (n ≤ 0.10N)
- Normality: Either population is normal OR n ≥ 30 (CLT)
Finding Values
- df = n − 1
- Look up in t-table using df and confidence level
Common values (df = large, approximately normal):
- 90% CI:
- 95% CI:
- 99% CI:
For small samples:
- df = 9, 95% CI: (larger than z)
- df = 4, 95% CI: (even larger)
Smaller df → larger → wider CI.
One-Sample Example
A random sample of 16 students has mean test score with sample SD s = 8. Find a 95% CI for the population mean.
Check conditions:
- Random sample ✓
- n = 16 < 30, but assume population approximately normal ✓
- Independence ✓
Calculate:
- From t-table: (95% CI, df = 15)
- CI:
Interpretation: We are 95% confident the mean score is between 73.7 and 82.3.
Two-Sample t-Interval
Comparing two population means and :
where:
(Use technology to find df; approximately or more complex formula)
Common Mistakes
- Confusing df: Always use df = n − 1, not n
- Using s instead of SE: The standard error is , not just s
- Wrong critical value: Look up , not z, from the t-table
- Forgetting conditions: Particularly the normality condition; state why it's satisfied
AP Exam Tip
Free-response questions often ask you to construct a t-interval. Show all steps: state formula, check conditions, identify , s, n, df, and , calculate ME, and state the CI with interpretation. Partial credit is generous if you show correct understanding.
📚 Practice Problems
1Problem 1easy
❓ Question:
What is a confidence interval and what does the confidence level represent?
💡 Show Solution
A confidence interval is a range of plausible values for a population parameter, calculated from sample data. The confidence level (e.g., 95%) represents the long-run success rate: if we repeated our sampling procedure many times and computed a confidence interval each time, approximately 95% of those intervals would contain the true population mean . A 95% CI does NOT mean there is a 95% probability that is in this specific interval; rather, the interval either contains or it does not.
2Problem 2medium
❓ Question:
Given a sample of 25 students with mean , sample standard deviation , construct a 95% confidence interval for the population mean.
💡 Show Solution
Conditions: Random sample, population approximately Normal or (assuming met). Formula: with . From t-table: . . CI: . We are 95% confident the population mean lies between 68.70 and 75.30.
3Problem 3hard
❓ Question:
Two researchers compute 90% confidence intervals for the same population mean. Researcher A uses , Researcher B uses . Whose interval is narrower? Explain why.
💡 Show Solution
Researcher B's interval is narrower. The margin of error is . Since appears in the denominator, larger produces smaller and thus smaller . For A: . For B: . Researcher B's standard error is half as large, so the margin of error is smaller, producing a narrower interval. This demonstrates why larger sample sizes provide more precise estimates—tighter confidence intervals.
⚠️ Common Mistakes: Confidence Intervals for Means
Avoid these 3 frequent errors
Practice with Flashcards
Rate this topic's cards with spaced repetition. Cards join your deck when you finish a topic's lesson and take its exit quiz.
Browse All Topics
Explore more AP Statistics topics