Data Handling
Data-handling questions test how raw data becomes usable information through validation, organisation, searching, sorting and presentation.
Data, information and processing
Data are raw symbols; information is processed data with meaning. Data handling covers collection, validation, verification, cleaning, storage, retrieval and presentation. Validation checks whether data is acceptable (type, range, length, format, presence, check digit); verification checks whether data matches its source, for example double entry or visual comparison.
Organising and searching
Lists or arrays store ordered elements of one type; records store related fields of different types. Sorting arranges data by a key; searching locates a value. Linear search checks elements in order and works on unsorted data. Binary search repeatedly halves a sorted list and is much faster for large datasets.
Worked example
Search for 23 in the sorted list [12, 15, 23, 27, 31, 45]. Indexes 0–5. Mid = (0+5)//2 = 2, value 23 → found in one comparison. If searching 31: mid 2 gives 23, target is larger, so low = 3; mid = (3+5)//2 = 4, value 31 → found in two comparisons. Binary search needs the sort order; applying it to unsorted data gives unreliable results.
Traps that cost marks
- Confusing validation with verification.
- Using binary search on unsorted data.
- Ignoring invalid or missing values during cleaning.
- Reporting a mean without checking outliers that distort it.
Presentation
Choose tables for exact values, bar charts for comparisons, line graphs for trends and pie charts for parts of a whole. Label axes, units and sources; avoid misleading truncated axes.