Author response image 2. Graphs exploring the influence of additional self-report error on the FH-AD effect.

(A) Simulated additional self-report error and the impact on the significance of the FH effect in MindCrowd. The effect of error in self-reported FH-AD status was simulated by adding additional error to the MindCrowd cohort by re-assigning individual FH responses between 1-30%. and re-running the full statistical model. This was repeated 10,000 times for each error rate. Boxplots representing the distribution of p-values for the re-analysis under the new error model are illustrated. This demonstrates that even with 8% additional error added to the self-report FH question, we would still have identified a significant association with FH in all 10,000 cases. Even with 24% additional FH self-report error we would have still reported a significant association between FH and PAL over 50% of the time (better than by chance alone). Therefore, this suggests that even with significant levels of additional error in FH self-report the association between FH and PAL performance would still have been noted by our study. (B) Permutation testing of the FH effect on PAL.The p-value describing the FH effect on PAL in MindCrowd was permuted to determine the probability of a false positive under the null hypothesis. The vertical dashed red line indicates our reported t-statistic from the manuscript and the histogram of permuted t-statistics are indicated in black bars. After one million permutations of the FH data label, not a single t-statistic arising from a permutation of the data were observed to be more extreme than our reported t-statistic (permuted p-value = <1e-6). This suggests that the odds of our finding being observed due to chance is less than one in one million.