How to Work with Types of Data and Tally Charts in IGCSE Statistics
Understanding how to classify data correctly is a core skill in IGCSE Statistics. This page focuses on how to identify categorical, discrete and continuous data, and how tally charts are used to organise survey results clearly and efficiently. The methods shown reflect the structure and terminology expected in exam questions.
Types of Data
Categorical data uses labels — no numbers involved. Discrete data is counted. Continuous data is measured.
What Each Type Means
Categorical
Data that describes labels or qualities. No numbers are involved, so you cannot calculate an average.
Categorical data cannot be counted or measured.
Examples: favourite subject, blood group (A, B, AB, O), colour of cars in a car park.
Discrete
Data that can only take certain specific values. Discrete data is counted.
Values are separate and distinct — there is nothing in between.
Examples: goals scored in a match (0, 1, 2...), number of passengers on a bus, shoe size.
Continuous
Data that can take any value within a range. Continuous data is measured.
A more precise instrument could always give a more precise reading.
Examples: height in cm, time to run a race, mass of a parcel.
The Key Question: Counted or Measured?
When you are unsure whether data is discrete or continuous, ask: was this value obtained by counting or by measuring?
- Counting always gives whole numbers or fixed steps — discrete. You cannot have 2.7 goals in a match.
- Measuring means the value could always be refined further with a more precise instrument — continuous. A time of 9.58 s could be recorded as 9.581 s, 9.5814 s, and so on.
Watch out for shoe sizes. Despite the half-sizes (6, 6.5, 7...), shoe sizes are discrete: only those fixed values exist. There is no shoe size 6.37.
Tally Charts
A tally chart is a quick way to record data as you collect it. Each item gets one vertical mark. When you reach five, draw a diagonal line through the previous four to make a bundle of five. This makes counting the total much faster.
Example: Types of Transport to School
A student surveys 28 classmates about how they travel to school each morning.
| Transport | Tally | Frequency |
|---|---|---|
| Walk | |||||||||| | 12 |
| Bus | ||||||| | 8 |
| Car | ||||| | 6 |
| Cycle | || | 2 |
| Total | 28 |
Always check: the frequency total should match the number of people surveyed.
🔑 Key Points
- Categorical data has no numbers: you cannot find a mean.
- Discrete data is counted: only specific values are possible.
- Continuous data is measured: any value in a range is possible.
- Tally groups of 5 make totalling fast and reduce errors.
- Always check your frequency total matches the number of data items collected.
⚠️ Common Mistakes
- Shoe sizes: despite the half-sizes, shoe sizes are discrete — only fixed values exist.
- Age: age as commonly stated ("I am 15") behaves as discrete, but the underlying measure of age is continuous.
- Tally counts: forgetting that the diagonal line is the 5th mark in a bundle.
- Frequency total: not checking the total against the number of data items collected.