Sample size 200
Completed / Failed 200 / 0
Which virtual sample size strategy best balances cross-tabulation stability and variance control in generative AI audience simulations?
Anchoring sample sizes to a fixed benchmark pool constructed from census microdata
91.5%
n=183
Respondents for this option · Drivers
Maximizing the representativeness of the simulated population
Ensuring statistical significance for granular sub-group analysis
Reducing the impact of model-induced variance and noise
Minimizing computational resource costs and latency
Utilizing lightweight panels of 100 to 300 agents for rapid concept screening
7.5%
n=15
Respondents for this option · Drivers
Minimizing computational resource costs and latency
Ensuring statistical significance for granular sub-group analysis
Maximizing the representativeness of the simulated population
Pursuing massive sample sizes in the tens of thousands to cover all edge segments
0.5%
n=1
Respondents for this option · Drivers
Ensuring statistical significance for granular sub-group analysis
Relying entirely on uncapped repeated sampling to offset model stochastic noise
0.5%
n=1
Respondents for this option · Drivers
Reducing the impact of model-induced variance and noise
Anchoring sample sizes to a fixed benchmark pool constructed from census microdata audience
Researchers favoring census-anchored sample sizes for generative AI simulations are primarily concentrated in the Northeast and South regions.
183 / 200 respondents91.5%
This segment shows a strong preference for using census-based microdata to anchor sample sizes for improved simulation stability.
The audience is characterized by a higher concentration of individuals aged 45-54 and those holding master's degrees compared to the general baseline.
Key differences
Potential risks
Sampling data