The post-2018 experimental evidence has strengthened rather than weakened the skeptical position on algorithmic causation of polarization.
Guess et al. (2023) found that replacing algorithmic feeds with chronological feeds for three months did not detectably change affective polarization.
Nyhan et al. (2023) found that reducing like-minded source exposure did not reduce affective polarization, ideological extremity, or candidate evaluations.
Guess et al. (2023) found that removing reshared content did not detectably affect political polarization despite substantially reducing political news exposure.
Argument from Sample to Population (Statistical Generalization)
Does the operational measure of F in the sample actually capture F as it is meant in the population-level claim?
Is the sample actually representative of the target population on the dimensions that matter for F (demographics, behavior, time period, platform mix)?
Is the sample large enough to support the precision (margin m) being claimed?
Does the conclusion stay within the population P from which S was drawn, or does it overreach (different country, different time period, different platform)?
Was the sample drawn or recruited in a way that systematically biases the proportion of F (e.g., volunteer bias, opt-in panels, attrition)?