Our demographic analysis of community-related variables yielded no significant differences based on participants’ age, ethnicity, or gender.
Lack of power or a tightly bounded null effect?
Note that even without “strong statistical significance” in standard tests we can meaningfully update our beliefs.
Of course we may need to adjust for multiple testing.
Also, statistical inference after machine learning presents some challenges. Relevant here?
We saw no systematic bias on a relevant order of magnitude. So yeah, we mean a null effect. Note the exception in Fig 3A: n=3 for non-binary respondents, where the sample size is very small.
Lack of power or a tightly bounded null effect? Note that even without “strong statistical significance” in standard tests we can meaningfully update our beliefs.
Of course we may need to adjust for multiple testing.
Also, statistical inference after machine learning presents some challenges. Relevant here?
We saw no systematic bias on a relevant order of magnitude. So yeah, we mean a null effect. Note the exception in Fig 3A: n=3 for non-binary respondents, where the sample size is very small.