Summary
In statistical sampling, information about a population is gathered through surveys like a census or sample survey. A census involves surveying every member of the population, while a sample survey covers less than 100% of the population. Random sampling is a method where every possible sample of size n has an equal chance of being selected, aiming to eliminate bias. The sampling frame is a complete list of all items or people in the population. Central Limit Theorem states that for large sample sizes, the distribution of sample means is approximately normal, regardless of the population's distribution. Unbiased estimates are sample statistics that equal the population statistics, like the sample mean being an unbiased estimate of the population mean.
Exam Tips
Key Definitions to Remember
- Population: The entire group you want information about.
- Census: Surveying every member of the population.
- Sample Survey: Surveying less than 100% of the population.
- Random Sampling: Each sample has an equal chance of being selected.
- Sampling Frame: Complete list of the population.
- Central Limit Theorem: Sample means distribution is normal for large samples.
- Unbiased Estimate: Sample statistic equals population statistic.
Common Confusions
- Confusing sample survey with census.
- Assuming random sampling always gives a representative sample.
Typical Exam Questions
- What is a census? A survey of every member of the population.
- How is a random sample selected? By giving each possible sample an equal chance of selection.
- What does the Central Limit Theorem state? It states that the distribution of sample means is approximately normal for large samples.
What Examiners Usually Test
- Understanding of different sampling methods.
- Ability to calculate unbiased estimates.
- Application of the Central Limit Theorem.