Other caveats:

* The tight, repeated loops tend to have perfect branch prediction rates and 100% L1 cache hit rates that are rarely present in real code.  

  There should be links to why this matters:

   - https://stackoverflow.com/questions/11227809
   - https://www.extremetech.com/extreme/188776-how-l1-and-l2-cpu-caches-work-and-why-theyre-an-essential-part-of-modern-chips

* Code timing list.append() or anything else that uses realloc() internally tends to be timing in clean environments where the realloc() extends in-place rather than recopying all the data.  This is almost never true in real code.

* Constant folding can produce misleading results.  

  To time addition, use:

      python -m timeit -s "x=2" "x+3"

  rather than:

      python -m timeit "2+3"

* Timings include global lookups, attribute lookups, and function call overhead in addition to the operation being timed.

  Use:

      python -m timeit -s "s=list(range(20))" -s "s_index=s.index" "s_index(5)"

  rather than:

      python -m timeit -s "s=list(range(20))"  "s.index(5)"

* Comparative timings should be repeated and interleaved to show whether the timings are noisy or have been affected the processor switching to a lower clock speed to avoid overheating:

      time a
      time b
      time a         # expect this to be consistent with the first *a* timing
      time b         # expect this to be consistent with the first *b* timing