Choose a random sample, spot bias, use a sample to estimate counts, means and totals for a whole population, judge which sample is more reliable, and (Higher) estimate a population by capture-recapture.
What you will learn
- Explain what makes a sample random, and why a sample is biased
- Use the fraction in a sample to estimate a count in the population
- Use a sample's mean, range and proportions to describe the population
- Combine samples, and say how to make an estimate more reliable
- Estimate a population by capture-recapture, and say what it assumes (Higher)
Population, sample and bias
The population is everyone (or everything) you want to know about.
A sample is the part you actually ask or measure. In a random sample every member of the population has the same chance of being chosen: number them all and pick with a random number generator.
A sample is biased when the way it is chosen makes some answers more likely than they are in the population. Asking people leaving a gym how much they exercise, or the first people to reply online, gives a biased sample. A bigger sample is more reliable, but only if it is random: a big biased sample is still biased.
The rest of this lesson and its 10 practice questions are locked.
In this lesson
- Population, sample and bias
- From a sample to the population
- Capture-recapture (Higher)
- Try it: First question
10 practice questions, marked as you go; about 25 minutes in all.
Key terms
Population, Sample, Random sample, Representative
In this unit: Probability and statistics: trees, experiments, samples and claims
More probability and statistics: tree diagrams in depth, expected outcomes from experiments, sampling and populations, critiquing charts and claims, and sample spaces in harder contexts.
- Tree diagrams in depth
- Expected outcomes and experiments
- Sampling and populations
- Critiquing charts and claims
- Sample spaces and combined events
All of Probability and statistics: trees, experiments, samples and claims