FAQ: Sampling Distributions - Sampling Distributions

This community-built FAQ covers the “Sampling Distributions” exercise from the lesson “Sampling Distributions”.

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This exercise can be found in the following Codecademy content:

Master Statistics with Python


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for this example of code, why is the range 500? shouldn’t the range be 50 because that’s the sample size?

sample_size = 50
# ... more code here ...
samp = np.random.choice(salmon_population, sample_size, replace = False)

The above statement picks 50 salmons randomly (without replacement i.e. the same fish won’t be selected more than once in the current sample) from the whole salmon population. This sample of 50 salmons is then assigned to the variable samp and (later further down in the code for the example), the mean of this sample is calculated. This is the first mean of a random sample of size 50.

Then, we repeat the sampling. 50 salmons are randomly picked from the whole salmon population. The mean of this sample is calculated. This is the second mean of a random sample of size 50.

The same process is repeated again and again.

The loop of range(500) is meant to carry out 500 repetitions of the above process. Nothing special about 500. We could repeat the process 600 times or 1000 times or some other number. Instead of just repeating the above process 10 or 20 times, doing so 500 times (or some other sufficiently large number) should give a reasonably decent picture of the distribution of the means. Do the means vary a lot or are the means fairly close together with a few outliers? The more times we select a random sample (of 50) and calculate the mean, the more data we have to arrive at an answer.