Confidence Intervals Notes
Angus Reid Strategies Survey on Evolution and Creationism
- 59% of Canadians believe in evolution.
- 42% agree that dinosaurs and humans co-existed, a creationist belief.
- Survey conducted on 1,088 Canadians with a margin of error of +3.0%, 19/20 confidence.
Confidence Intervals
- Definition: An interval of values within which the true population mean likely falls.
- Confidence intervals account for sampling errors, providing upper and lower limits around the sample mean.
Constructing Confidence Intervals
- Sample mean is a starting point for estimating population mean.
- Example: Population mean of male Dawson students' heights between 5ft. and 6ft.
- Confidence levels (common choices):
- 95% (z = 1.96)
- 99% (z = 2.58)
Calculating Confidence Intervals when Population Standard Deviation is Known
- Formula: CI = ext{Sample Mean} \, ext{±} \, z imes ext{Standard Error}
- Standard Error (SE) when sample size n is known: SE = rac{ ext{Standard Deviation}}{ ext{sqrt}(n)}
Example Calculation
- Sample: 225 students, mean = 606, population SD = 100
- SE = rac{100}{ ext{sqrt}(225)} = 6.67
- 95% CI = 606 ± (1.96 imes 6.67) = 606 ± 13.13
- CI = [592.87, 619.13]
Standard Error of the Mean
- Definition: Average distance of sample means from the population mean.
- Calculating SE: SE = rac{ ext{Standard Deviation}}{ ext{sqrt}(n)}
Confidence Intervals When Population Standard Deviation is Unknown
- Use t distribution instead of z-scores.
- Formula changes to: CI = ext{Sample Mean} \, ext{±} \, t imes S_x
- Estimate standard error with sample standard deviation: S_x = rac{s}{ ext{sqrt}(n-1)}
Example with t-distribution
- Mean = 100, SD = 12, Sample size = 16
- Standard Error = rac{12}{ ext{sqrt}(15)}; Find appropriate t value (based on degrees of freedom).
Confidence Intervals for Proportions
- Formula: CI = P \, ext{±} \, Z imes s_p
- Where s_p = ext{sqrt}rac{P(1-P)}{n}
- Calculate confidence interval for a sample proportion (e.g. 40% of students working 20 hours/week).
Final Note
- Confidence intervals provide a range of values for estimating population parameters.
- Use appropriate methods depending on what is known (standard deviation vs sample statistics).