Statistical Inference
Statistical inference uses sample data to draw conclusions about a population, applying confidence intervals and understanding sampling variability.
What You Need to Know
Key Concept Diagram
A sample is used to estimate population parameters when it is impractical to survey everyone
Sampling variability means different samples give different statistics
A confidence interval gives a range of plausible values for a population parameter
Larger sample sizes reduce variability and produce narrower confidence intervals
Bias occurs when sampling methods systematically favour certain outcomes
Key Vocabulary
Sampling variability
The natural differences in statistics computed from different samples of the same population
Confidence interval
A range of values calculated from sample data that is likely to contain the true population parameter
Population parameter
A numerical characteristic of an entire population, such as mean or proportion
Bias
A systematic error in data collection that causes results to consistently differ from the truth
Knowledge Check
Select the correct answer for each question. Click "Check Answer" to see if you are right.
Question 1
A survey of 50 students finds 60% prefer online learning. What does this 60% represent?
Question 2
Which of the following reduces sampling variability?
Question 3
A confidence interval for the mean height is (165 cm, 172 cm). What does this mean?
Key Concepts Summary
- ●A sample is used to estimate population parameters when it is impractical to survey everyone
- ●Sampling variability means different samples give different statistics
- ●A confidence interval gives a range of plausible values for a population parameter
- ●Larger sample sizes reduce variability and produce narrower confidence intervals
- ●Bias occurs when sampling methods systematically favour certain outcomes