Summary
Data presentation and interpretation involves understanding how to categorize and display data effectively.
- Qualitative data — data that describes attributes or characteristics. Example: Hair colour, Blood type, Ethnic group
- Quantitative data — data that involves counting or measuring attributes. Example: Height, Weight, Number of people living in your town
- Discrete data — quantitative data with a finite number of values. Example: Number of students in a class
- Continuous data — quantitative data with infinite possible values within a range. Example: Temperature range
- Stem-and-leaf diagram — a method of displaying data that retains original values. Example: Runs scored by two batsmen in cricket matches
- Histogram — a graphical representation of data using bars of different heights. Example: Diameters of components from a production line
- Cumulative frequency graph — a plot showing the cumulative totals of data. Example: Reaction times of participants in an experiment
Exam Tips
Key Definitions to Remember
- Qualitative data
- Quantitative data
- Discrete data
- Continuous data
- Stem-and-leaf diagram
- Histogram
- Cumulative frequency graph
Common Confusions
- Confusing discrete data with continuous data
- Misinterpreting histograms with unequal class widths
Typical Exam Questions
- What is the difference between qualitative and quantitative data? Qualitative data describes attributes, while quantitative data involves measurements.
- How do you construct a stem-and-leaf diagram? Arrange stems vertically and leaves horizontally, including a key.
- How is a histogram different from a bar chart? Histograms represent continuous data with no gaps between bars.
What Examiners Usually Test
- Ability to classify data as qualitative or quantitative
- Construction and interpretation of stem-and-leaf diagrams
- Drawing and interpreting histograms and cumulative frequency graphs