A starting point for discussion
The idea
To check whether their sample was large enough to give stable estimates of preferences, the researchers compared equal-sized, non-overlapping random samples. They measured how closely the proposal win rates in the two samples moved together, a relationship called correlation.
Place in the reasoning
AI assessment · Connected to reasoning
Describes a split-sample stability check on proposal win rates, relevant to the distinction between stable estimates within a study and population representation.
Consistent study results may not reflect the wider public
The check assesses stability of preference estimates in participant data, not representativeness of the population or accuracy beyond that setting.
What this means and why it matters
What this means
The researchers split participants into two random groups of equal size and compared how closely the groups’ proposal win rates matched. R² is a measure of how closely the rates in one group tracked those in the other.
Why it matters
This checks whether preferences gathered from groups look similar as sample size changes; it does not establish that the participants represent the population the study aimed to represent.
Conditions & assumptions
Source coverage: Full readable text of the retrieved 37-page research draft, including references and appendices. Figures are not independently interpreted. They used Pearson correlation, a measure of how closely two sets of values move together, and its coefficient of determination (R²), which shows how closely the proposal win rates in the two samples track each other. They plotted the result against sample size. Addresses: Appendix B data adequacy checks
Questions this connects to
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Related claims & perspectives
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