Guess et al. (2023) null findings do not rule out algorithmic effects below the study's minimum detectable effect size or effects on non-vol
Guess et al. (2023) null findings do not rule out algorithmic effects below the study's minimum detectable effect size or effects on non-volunteer subpopulations with different baseline polarization levels.
Absence-of-evidence arguments require specification of the minimum detectable effect size and acknowledgment that non-detection does not prove absence, particularly when volunteer bias may suppress effects.
Guess et al. (2023) conducted a large field experiment with consenting volunteers, introducing volunteer bias that may reduce or mask treatment effects relative to the full population.
The framing's ≥10% bar requires evidence that effects do not reach that threshold; null results in volunteer samples do not establish this across the full population.
Argument from Lack of Evidence (Negative Evidence)