FAQ: Hypothesis Testing - Dangers of Multiple T-Tests


This community-built FAQ covers the “Dangers of Multiple T-Tests” exercise from the lesson “Hypothesis Testing”.

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This exercise can be found in the following Codecademy content:

Data Science

FAQs on the exercise Dangers of Multiple T-Tests

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Hi there, when I input tts, a_b_pval = ttest_ind(a, b), the result of the a_b_pval is 2.76676293987e-05, which clearly is not a p-value. What gone wrong?


2.76676293987e-05 is scientific notation for 0.0000276676293987, which is a p-value showing that the result of that particular t-test has an exceptionally good chance at being significant.


What error are we calculating using the error probability function provided? As I understand it, the p-value is essentially the probability of a type 1 error. So, over multiple t-tests:

p(type 1 error) = p_value_1 * p_value 2 * … * p_value_n


p(not(type 1 error)) = 1 - (p_value_1 * p_value 2 * … * p_value_n).

In this way, multiple t-tests would actually decrease your chance of a type 1 error.

I think my issue here actually boils down to two questions. First, what source of error does the provided error probability function calculate? Second, can p(statistical significance) decrease while p(type 1 error) also decreases?