FAQ: Sample Size Determination with Simulation - Estimating Power

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This community-built FAQ covers the “Estimating Power” exercise from the lesson “Sample Size Determination with Simulation”.

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This exercise can be found in the following Codecademy content:

Master Statistics with Python

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[gavingro]

On this page we are instructed to turn the results list into a numpy array, then use numpy methods to count all the rows that have the “significant” entry.

Is there any reason why it is important to turn the list into an array before counting the results?
Wouldn’t results.count("significant") get us there without the extra step?

I’m imagining this has something to do with optimization since I imagine lists methods are slow and numpy methods are fast, but I’d love some clarification.

[bdangerf]

That is an interesting theory @gavingro for why the exercise asks us to convert to numpy array. I also used results.count("significant") and came here to see if anyone else did… glad I wasn’t the only one.

I think changing the data structure is slower, and prefer the builtin method of .count()