FAQ: Significance Thresholds - Problems with Multiple Hypothesis Tests

This community-built FAQ covers the “Problems with Multiple Hypothesis Tests” exercise from the lesson “Significance Thresholds”.

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This exercise can be found in the following Codecademy content:

Master Statistics with Python

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Heya, I’m trying to understand why a test would necessarily have the probability of producing a false positive. Is it because we’re considering it improbable that the value in question comes from the Null Hypothesis’s binomial distribution, while it’s technically still possible that it does?

For instance, let’s say our significance threshold was 5%. That would mean we’re going to assume (with good warrant) that any outcome with a probability of 5% or less comes from a distribution with a different mean, but there’s still technically a 5% chance that it came from the distribution/mean of our Null Hypothesis. So, by supposing it came from another mean, we’re discounting the 5% probability that it did actually come from the Null Hypothesis, and so there’s still a 5% chance that our declaration of ‘significance’ is wrong? Is that right?