Population, Types of Data & Sampling

 

What Are the Different Types of Data?

Primary Data

• Collected first-hand by the researcher.
• Methods: surveys, interviews, experiments.
• Example: asking pupils how many hours they study each night.

Secondary Data

• Collected by someone else, previously.
• Found in textbooks, websites, reports.
• Example: data from a government report or scientific article.

 

Qualitative Data

• Descriptive data in words (not numbers).
• Example: favourite colour, type of pet, brand of cereal.

Quantitative Data

• Data given in numbers.
• Can be discrete or continuous.

Discrete Data

• Counted values.
• Takes specific values only.
• Example: number of books read.

Continuous Data

• Measured values.
• Can take any value within a range.
• Example: weight of a watermelon, temperature of a room.

 

What Is a Population?

A population is the entire group you are studying.

• Example 1: All Year 11 students at a school.
• Example 2: All makes of mobile phones in a store.

Important: A population doesn’t have to be people. It could be objects, animals, or anything measurable.

 

What Is a Sample?

A sample is a small group taken from the population.

• Used to gather data more quickly and cheaply than from the whole population.

Random Sample

• Every member of the population has an equal chance of being chosen.

Biased Sample

Some members are more likely to be chosen than others.

Example: only asking pupils from one class.

 

Advantages and Disadvantages

Population (Census)

Advantages:

• Accurate
• Includes all responses/options

Disadvantages:

• Time-consuming
• Expensive
• Too much data to process

 

Sample

Advantages:

• Quicker
• Cheaper
• Easier to handle

Disadvantages:

• Might not be accurate
• Can be biased
• Different samples may give different results

 

Example

Mike is a biologist studying 600 mice in an enclosure. He selects 10 mice nearest the entrance for a drug trial.

(a) What is the population?

• All 600 mice in the enclosure.

(b) Two problems with Mike’s method:

• Small sample — only 10 mice.
• Biased selection — he only chooses mice near the entrance.

(c) How could Mike improve his sampling?

• Use a larger sample for more reliable results.
• Select mice at random to avoid bias.