How do you reason about collection complexity without choosing a data structure from Big O notation alone?

Collection operation complexity and real runtime costs

Uses complexity as a first filter while accounting for constants, locality, allocations, data size, and the complete operation sequence.

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TL;DR

I start with the dominant operations and expected data size. A list scan is O(n), a dictionary lookup is O(1) on average, sorting is usually O(n log n), and inserting into the middle of a list is O(n). Those labels describe growth, not the complete runtime cost. For small collections, a simple contiguous scan can still be the clearest and fastest practical choice.

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Big O, amortized cost, lookup and mutation sequences, constants, locality, allocation, memory overhead, and measurement.

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Why this question matters
Complexity gives useful bounds, but good collection choices come from the complete workload and the sizes the application actually handles.
I start with the dominant operations and expected data size. A list scan is O(n), a dictionary lookup is O(1) on average, sorting is usually O(n log n), and inserting into the middle of a list is O(n). Those labels describe growth, not the complete runtime cost. For small collections, a simple contiguous scan can still be the clearest and fastest practical choice.
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I evaluate the whole sequence. Building a dictionary costs O(n), memory, and hashing, so it may be wasteful for one lookup over ten items but valuable for thousands of repeated lookups. A binary search needs sorted data and then gives O(log n) lookup. Appending to a list is amortized O(1) because occasional growth copies the backing array. I include duplicate policy, ordering, mutation frequency, and whether the collection must be built once or rebuilt for every request.

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Quoting Big O without naming the operations.
Ignoring the one-time cost of building an index.
Treating average and worst-case complexity as identical.
Benchmarking tiny synthetic data that does not represent production.

How do you reason about collection complexity without choosing a data structure from Big O notation alone?

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