He received his Doctor of Dental Surgery (DDS) from Hokkaido University in 2020, followed by a Doctor of Philosophy (Ph.D.) in Dental Science from the same institution in 2025. Upon completing his doctoral degree, he directly transitioned to independent research in mathematical statistics. Under conditions where Poisson noise, overdispersion, or additive instrumental errors occur, the process of composition (scaling by the total sum) propagates these distortions asymmetrically across all components, making the value of each component difficult to compare due to noise contamination. As a simple degree-of-freedom check or limit analysis (N → ∞ or N → 0) easily proves, compositional data is fundamentally closed and isolated within a single row (each sample), demonstrating the inherent impossibility of direct cross-sample comparisons. In the presence of such noise, any attempt to use the central limit theorem and Chebyshev's inequality to define a boundary between signal and noise fails, as the full distribution remains unknown and the setting of the threshold parameter is purely an arbitrary matter of human interpretation. Consequently, even via proof by contraposition, every individual component within a compositional dataset remains completely indistinguishable from noise. The only rational procedure to escape this epistemological dead end is to perform an alternative measurement.