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Personality researchers have posited multiple ways in which the relations between personality traits and life outcomes may be moderated by other traits, but there are well-known difficulties in reliable detection of such trait-by-trait interaction effects. Estimating the prevalence and magnitude base rates of trait-by-trait interactions would help to assess whether a given study is suited to detect interaction effects. We used the Life Outcomes of Personality Replication Project dataset to estimate the prevalence, nature, and magnitude of trait-by-trait interactions across 81 self-reported life outcomes ( n ≥ 1350 per outcome). Outcome samples were divided into two halves to examine the replicability of observed interaction effects using both traditional and machine learning indices. The study was adequately powered (1 − β ≥ .80) to detect the smallest interaction effects of interest (interactions accounting for a Δ R2 of approximately .01) for 78 of the 81 (96%) outcomes in each of the partitioned samples. Results showed that only 40 interactions (5.33% of the original 750 tests) showed evidence of strong replicability through robustness checks (i.e., demographic covariates, Tobit regression, and ordinal regression). Interactions were also uniformly small in magnitude. Future directions for research on trait-by-trait interactions are discussed.