Community resourceWorksheet
OCR H446 2.3.1 Algorithm suitability, time and space
Part 2 of 16 · H446 2.3.1 · Algorithms
A correct algorithm can still be the wrong one to use, and H446 2.3.1 expects students to argue suitability against execution time, memory and the situation the algorithm runs in. This worksheet gives students a repeatable comparison frame so recommendations are tied to data and context rather than a claim that one method is simply faster.
Students will:
- distinguish whether an algorithm is correct from whether it is suitable here
- describe how dominant work and extra storage grow with the number of items
- weigh preprocessing and update costs against the number of times a task is repeated
- recommend an approach for a repeatedly searched school list that changes overnight
- justify a choice for a memory-limited sensor that receives one reading at a time
Inside: 6 explanation cells, 1 multiple-choice question, 1 fill-in-the-blanks cell and 3 written answers. 22 marks, about 20 to 30 minutes.
Series: H446 2.3.1 · Algorithms, part 2 of 16.
Shared by Coding PathwayVerified teacher
- 11 cells
- About 30 minutes
- CC BY-SA 4.0
- Shared 31 Aug 2026
- Updated 3 Sept 2026
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The whole resource, exactly as a class sees it. Answers and marking are held back.
Algorithm suitability, time and space
A correct algorithm may still be unsuitable. Compare candidates on the same task, data and resource constraints.
By the end, you will be able to
- distinguish correctness from suitability;
- count dominant work and auxiliary storage;
- compare algorithms across data sizes/distributions;
- make a qualified recommendation.
Reactivate: input size n describes the amount of data.
Worked comparison
To find an ID in 1,000 unsorted records, linear search needs no preprocessing and may inspect 1,000 records. Sorting first enables binary search, but sorting has a cost. For one search, linear may be suitable; for repeated searches on stable data, sort once then binary-search many times may repay the setup.
Time means growth in operations, not stopwatch brand. Space means additional working storage, not input size alone.
Why can the most suitable search change between scenarios?
- ACorrectness is optional
- BPreprocessing and number of searches differ
- CBinary search works on unsorted data
- DSpace never matters
Algorithm A scans n items once and stores one running maximum. Algorithm B copies n items, then compares every pair. Describe the time and extra-space growth of each.
Identify dominant repeated work.
Students type their answer here.
Guided recommendation frame
- State the required result and preconditions. 2. Compare time on relevant data. 3. Compare extra space. 4. Include setup/update costs. 5. Reach a scenario-linked conclusion. Avoid saying one algorithm is simply fastest.
A school searches a student list 2,000 times daily; records change once overnight. Compare keeping it unsorted for linear search with sorting overnight for binary search.
Address setup, repeated queries and ordered precondition.
Students type their answer here.
Independent transfer
A sensor has little memory and receives one value at a time. Compare storing all readings then finding a maximum with updating one running maximum.
Evaluate both methods and recommend one. Include correctness, time, extra space and one case where storing all values is still needed.
Compare on common criteria.
Students type their answer here.
Closed-book checkpoint
Complete each sentence from memory. There is no answer bank and correctness is held for teacher review.
Review your understanding
Before submitting, check that you can explain the central distinction in your own words, expose the intermediate state that supports your answer and apply the method in an unfamiliar context.