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Success on many tasks depends on a trade-off between speed and accuracy. In a novel variant, a speed-accuracy trade-off with sample-based decisions in which both speed and accuracy jointly depend on (self-truncated) sample size, we found strong accuracy biases. On every trial of a sequential investment game, participants chose between 2 investment funds based on binary samples of the funds’ past outcomes. Participants could stop sampling and decide whenever they felt sufficiently informed. Total payoff was the product of choice accuracy and number of choices completed within the available time (speed). Participants’ failure to understand the dominance of speed over accuracy—that speed decreases more than accuracy improves with increasing sample size—led to dramatic oversampling. Our research aimed to examine to what extent metacognitive functions of monitoring and control could correct for the accuracy bias. Experiments 1a through 1c demonstrated similarly strong accuracy biases and payoff losses in psychology and economics students, depressed, and control patients. In Experiments 2 through 4, the accuracy bias persisted despite several manipulations (feedback, sample limit, choice difficulty, payoff, sampling truncation as default) that underlined the speed advantage, reflecting a conspicuous metacognitive deficit. Even when participants faced no risk of losing on incorrect trials but could still win on correct trials (Experiment 3) and when sampling was contingent on the active solicitation of every new element (Experiment 4), participants continued to sample too much and failed to overcome the accuracy bias. The final discussion focuses on psychological reasons and possible remedies for the metacognitive deficit in trade-off regulation.

Self-enhancement conceived as a positive relation between happiness (H) and the difference S-O (selfrating S minus objective measure O) is inherently confounded with a positive self-view account: S alone may explain the positive relation to H. Condition-based regression analysis (CRA;Humberg et al., 2018aHumberg et al., , 2018b) promises a solution. CRA assumes that opposite predictor weights (b S . 0; b O , 0) in regression of H on S and O rule out that H depends on S alone. However, despite the truism that two significant regression weights imply that both S and O contribute to the prediction model, they cannot rule out a positive self-view account. If only S shares variance with H, O may improve the prediction indirectly, by suppressing unpredictive S-variance in the prediction model. Granting S-O-redundancy, a classical suppressor effect (Conger & Jackson, 1972) results in a negative regression weight for O (binding S-variance that is unpredictive of H). Thus, the regression pattern that CRA presumes to rule out a positive self-view account indeed follows necessarily from a suppressor effect entailed in a positive self-view account. Computer simulations illustrate and corroborate this critique.

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