This is a comprehensive and in-depth post! “The best analysis protect clarity” is well-summarized. More granularity sometimes may reduce clarity, not improve it. This made me think about medical research, an appropriate sample size (N) renders the research more efficient and the insight or conclusion more reliable. Analytical values rise with sample size at first, but after the study is adequately powered, further increases in detail mostly improve precision rather than insight, and sometimes may even create misleading statistical significance for clinically trivial effects. Very interesting analysis!
I love the comparison to medical research. There's definitely a diminishing return on granularity. At some point, we're no longer improving understanding, we're just narrowing confidence intervals around a conclusion we already had.
Clarity is often a signal that we've found the right level of detail.
This is a comprehensive and in-depth post! “The best analysis protect clarity” is well-summarized. More granularity sometimes may reduce clarity, not improve it. This made me think about medical research, an appropriate sample size (N) renders the research more efficient and the insight or conclusion more reliable. Analytical values rise with sample size at first, but after the study is adequately powered, further increases in detail mostly improve precision rather than insight, and sometimes may even create misleading statistical significance for clinically trivial effects. Very interesting analysis!
I love the comparison to medical research. There's definitely a diminishing return on granularity. At some point, we're no longer improving understanding, we're just narrowing confidence intervals around a conclusion we already had.
Clarity is often a signal that we've found the right level of detail.