~/analysis/interpretation

Every timing, measured on the same 3,432 records.

The runtime table’s results came from a dataset of 3,432 registrations. Stating the dataset matters because it puts the measured runtimes in context and lets the results be interpreted fairly.

3,432 registrationsFair comparisonReproducible
01 · Why the dataset matters

Efficiency depends on input size.

Because an algorithm’s efficiency depends on the size of its input, a runtime means little without knowing how much data it processed.

01

Realistic conditions

Reporting that every timing used 3,432 records shows the analysis ran under realistic, standard conditions.

02

Fair comparison

With the same input for every algorithm, the sorting algorithms can be compared fairly.

03

Reproducible

The results are easier to reproduce if the same dataset is used again.

02 · The numbers in context

At n = 3,432.

17ms
Merge Sort · O(n log n)
22ms
Quick Sort · O(n log n)
26ms
Insertion Sort · O(n²)
45ms
Selection Sort · O(n²)

Full table: Runtime measurement.