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| author | Christian Cleberg <hello@cleberg.net> | 2024-07-26 11:13:24 -0500 |
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| committer | Christian Cleberg <hello@cleberg.net> | 2024-07-26 11:13:24 -0500 |
| commit | c6a33b78b9a75f5105a6847da9677c014327242e (patch) | |
| tree | fd1a8a496b4aa3d23bac6241eae0ef871c29c4e3 /content/blog/2021-08-25-audit-sampling.md | |
| parent | bac5bae4327a6ccb2545d445a399c18de1aa9746 (diff) | |
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| -rw-r--r-- | content/blog/2021-08-25-audit-sampling.md | 2 |
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diff --git a/content/blog/2021-08-25-audit-sampling.md b/content/blog/2021-08-25-audit-sampling.md index c2d3c1d..90562c6 100644 --- a/content/blog/2021-08-25-audit-sampling.md +++ b/content/blog/2021-08-25-audit-sampling.md @@ -66,7 +66,7 @@ generation and will allow you to reproduce a sample, given that nothing else changes. This is critical to the posterity of audit work. After all, how can you say your audit process is adequately documented if the next person can't run the code and get the same sample? The final piece here on randomness is to look at -the [choice](https://docs.%20python.org/3/library/random.html#random.choice) +the [choice](https://docs.python.org/3/library/random.html#random.choice) function used above. This is the crux of the generation and can also be examined for more detailed analysis on its reliability. As far as auditing goes, we will trust that these functions are mathematically random. |