Types of data and sampling
Types of data
| Type | Meaning | Example |
|---|---|---|
| described in words | favourite sport | |
| numbers | height | |
| counted: only certain values | number of pets, shoe size | |
| measured: any value in a range | time, mass |
Data is if you collect it yourself, and if someone else collected it (such as a website or a census).
Example: classify each: the time to run 100 m; the number of people in a car; eye colour.
- Time is measured: quantitative and continuous
- People are counted: quantitative and discrete
- Eye colour is a word: qualitative
Key terms
Populations and samples
The is the whole group you want to know about. A is part of it, chosen to save time and money.
- In a random sample, every member of the population has the same chance of being chosen.
- A sample is if it isn't fair: some groups are more likely to be picked, so it doesn't represent the population.
- A bigger sample usually gives more reliable results.
Example: a school has 1200 students. Describe how to pick a random sample of 50.
- Number the students 1 to 1200
- Use a random number generator to pick 50 numbers, ignoring repeats
- Survey the students with those numbers
Asking only students in the library about reading would be biased: they probably read more than most.
Key terms
Capture–recapture (Higher tier only)
Capture–recapture estimates the size of an animal population.
- Catch a sample, mark them, and release them.
- Later, catch a second sample and count how many are marked.
- Estimate:
Example: 40 fish are caught, tagged and released. Later, 50 fish are caught, and 8 of them are tagged. Estimate the number of fish in the lake.
- N = 40 × 50 ÷ 8
- = 2000 ÷ 8
- About 250 fish
The method assumes the population doesn't change in between (no births, deaths or moving), the marks don't come off, and the marked animals mix back in evenly.