Overview
This lecture covers the concept of random sampling in statistics, focusing on the definition, importance, and practical methods for creating a simple random sample (SRS).
Random Samples in Statistics
- A random sample means every person in the population has a chance to be included in the sample.
- Sampling is not a census; you only collect data from a subset, not the entire population.
- The goal is for everyone in the population to have an equal chance of being chosen, minimizing bias.
Simple Random Sample (SRS)
- Simple random sample (SRS) is the most common method for random sampling in statistics.
- SRS requires numbering everyone in the population to ensure each has a chance of selection.
- Random number generators (computers or random number tables) are used to select sample members.
- SRS minimizes bias if properly implemented and is the preferred method unless a census is possible.
Practical Examples of SRS
- At a college, use student ID numbers and a random number generator to select students.
- For national surveys, use Social Security Numbers for random computer selection.
- In business, randomly select names from a column using computer programs.
- Another method is to write names on equally sized papers, mix them in a box, and draw them randomly (like a lottery).
Misconceptions about Randomness
- Walking through a mall and choosing people is not statistically random since not everyone in the population could be selected.
- Humans cannot truly generate random samples by simply pointing or choosing at will; mechanical or computerized methods are needed.
Key Terms & Definitions
- Random Sample — A sample where every member of the population has a chance to be chosen.
- Simple Random Sample (SRS) — A sampling method where every individual has an equal chance of selection, usually through numbering and random generators.
- Bias — Systematic error that skews results away from true population characteristics.
- Census — Data collection from the entire population, not just a sample.
Action Items / Next Steps
- Review additional methods of data collection in the next lecture/session.