Joseph Simmons

Joseph Simmons (2023), Are People More or Less Likely To Follow Advice That Is Accompanied By A Confidence Interval?, Journal of Experimental Psychology: General, 152 (July), pp. 2008-2025.

Abstract: Are people more or less likely to follow numerical advice that communicates uncertainty in the form of a confidence interval? Prior research offers competing predictions. Although some research suggests that people are more likely to follow the advice of more confident advisors, other research suggests that people may be more likely to trust advisors who communicate uncertainty. Participants (N = 17,615) in 12 incentivized studies predicted the outcomes of upcoming sporting events, the preferences of other survey responders, or the number of deaths due to COVID-19 by a future date. We then provided participants with an advisor’s best guess and manipulated whether or not that best guess was accompanied by a confidence interval. In all but one study, we found that participants were either directionally or significantly more likely to choose the advisor’s forecast (over their own) when the advice was accompanied by a confidence interval. These results were consistent across different measures of advice following and did not depend on the width of the confidence interval (75% or 95%), advice quality, or on whether people had information about the advisor’s past performance. These results suggest that advisors may be more persuasive if they provide reasonably-sized confidence intervals around their numerical estimates.

Beidi Hu and Joseph Simmons (2023), Does Constructing A Belief Distribution Truly Reduce Overconfidence?, Journal of Experimental Psychology: General, 152 (2), pp. 571-589.

Abstract: Can overconfidence be reduced by asking people to provide a belief distribution over all possible outcomes – that is, by asking them to indicate how likely all possible outcomes are? Although prior research suggests that the answer is “yes,” that research suffers from methodological confounds that muddle its interpretation. In our research, we remove these confounds to investigate whether providing a belief distribution truly reduces overconfidence. In 10 studies, participants made predictions about upcoming sports games or other participants’ preferences, and then indicated their confidence in these predictions using rating scales, likelihood judgments, and/or incentivized wagers. Contrary to prior research, and to our own expectations, we find that providing a belief distribution usually increases overconfidence, because doing so seems to reinforce people’s prior beliefs. 

Uri Simonsohn, Joseph Simmons, Leif D. Nelson (2022), Above averaging in literature reviews, Nature Reviews Psychology, 1 (9), pp. 1-2.

Abstract: Meta-analysts’ practice of transcribing and numerically combining all results in a research literature can generate uninterpretable and/or misleading conclusions. Meta-analysts should instead critically evaluate studies, draw conclusions only from those that are valid and provide readers with enough information to evaluate those conclusions.

Celia Gaertig and Joseph Simmons (2021), The Psychology of Second Guesses: Implications for the Wisdom of the Inner Crowd, Management Science, 67 (September) (), pp. 5921-5942.

Abstract: Prior research suggests that averaging two guesses from the same person can improve quantitative judgments, a phenomenon known as the “wisdom of the inner crowd.” In this article, we find that this effect hinges on whether people explicitly decide in which direction their first guess had erred before making their second guess. In nine studies (N = 8,465), we found that asking people to explicitly indicate whether their first guess was too high or too low prior to making their second guess made people more likely to provide a second guess that was more extreme (in the same direction) than their first. As a consequence, the introduction of that “Too High/Too Low” question reduced (and sometimes eliminated or reversed) the wisdom-of-the-inner-crowd effect for (the majority of) questions with non-extreme correct answers and increased the wisdom-of-the-inner-crowd effect for questions with extreme correct answers. Our findings suggest that the wisdom-of-the-inner-crowd effect is not inevitable, but rather that it depends on the processes people use to generate their second guesses.

Joowon Klusowski, Deborah Small, Joseph Simmons (2021), Does Choice Cause an Illusion of Control?, Psychological Science, 32 (February) (), pp. 159-172.

Abstract: Previous research suggests that choice causes an illusion of control—that it makes people feel more likely to achieve preferable outcomes, even when they are selecting among options that are functionally identical (e.g., lottery tickets with an identical chance of winning). This research has been widely accepted as evidence that choice can have significant welfare effects, even when it confers no actual control. In this article, we report the results of 17 experiments that examined whether choice truly causes an illusion of control (N = 10,825 online and laboratory participants). We found that choice rarely makes people feel more likely to achieve preferable outcomes—unless it makes the preferable outcomes actually more likely—and when it does, it is not because choice causes an illusion but because choice reflects some participants’ preexisting (illusory) beliefs that the functionally identical options are not identical. Overall, choice does not seem to cause an illusion of control.

Joseph Simmons, Leif D. Nelson, Uri Simonsohn (2021), Pre-registration Is A Game Changer. But, Like Random Assignment, It Is Neither Necessary Nor Sufficient For Credible Science, Journal of Consumer Psychology, 31 (January) (), pp. 177-180.

Abstract: We identify 15 claims Pham and Oh (2020) make to argue against pre-registration. We agree with 7 of the claims, but think that none of them justify delaying the encouragement and adoption of pre-registration. Moreover, while the claim they make in their title is correct—pre-registration is neither necessary nor suffi- cient for a credible science—this is also true of many our science’s most valuable tools, such as random assignment. Indeed, both random assignment and pre-registration lead to more credible research. Pre-registration is a game changer.

Joseph Simmons, Leif D. Nelson, Uri Simonsohn (2021), Pre-registration: Why and How, Journal of Consumer Psychology, 31 (January) (), pp. 151-162.

Abstract: In this article, we (1) discuss the reasons why pre-registration is a good idea, both for the field and individual researchers, (2) respond to arguments against pre-registration, (3) describe how to best write and review a pre-registration, and (4) comment on pre-registration’s rapidly accelerating popularity. Along the way, we describe the (big) problem that pre-registration can solve (i.e., false positives caused by p-hacking), while also offering viable solutions to the problems that pre-registration cannot solve (e.g., hidden confounds or fraud). Pre-registration does not guarantee that every published finding will be true, but without it you can safely bet that many more will be false. It is time for our field to embrace pre-registration, while taking steps to ensure that it is done right.

Uri Simonsohn, Joseph Simmons, Leif D. Nelson (2020), Specification Curve Analysis, Nature Human Behaviour, 4 (November) (), pp. 1208-1214.

Abstract: Empirical results hinge on analytical decisions that are defensible, arbitrary and motivated. These decisions probably introduce bias (towards the narrative put forward by the authors), and they certainly involve variability not reflected by standard errors. To address this source of noise and bias, we introduce specification curve analysis, which consists of three steps: (1) identifying the set of theoretically justified, statistically valid and non-redundant specifications; (2) displaying the results graphically, allowing readers to identify consequential specifications decisions; and (3) conducting joint inference across all specifications. We illustrate the use of this technique by applying it to three findings from two different papers, one investigating discrimination based on distinctively Black names, the other investigating the effect of assigning female versus male names to hurricanes. Specification curve analysis reveals that one finding is robust, one is weak and one is not robust at all.

Joshua Lewis and Joseph Simmons (2020), Prospective Outcome Bias: Incurring (Unnecessary) Costs to Achieve Outcomes That Are Already Likely, Journal of Experimental Psychology: General, 149 (), pp. 870-888.

Abstract: How do people decide whether to incur costs to increase their likelihood of success? In investigating this question, we offer a theory called prospective outcome bias. According to this theory, people tend to make decisions that they expect to feel good about after the outcome has been realized. Because people expect to feel best about decisions that are followed by successes—even when the decisions did not cause those successes—they will pay more to increase their chances of success when success is already likely (e.g., people will pay more to increase their probability of success from 80% to 90% than from 10% to 20%). We find evidence for prospective outcome bias in nine experiments. In Study 1, we establish that people evaluate costly decisions that precede successes more favorably than costly decisions that precede failures, even when the decisions did not cause the outcome. Study 2 establishes, in an incentive-compatible laboratory setting, that people are more motivated to increase higher chances of success. Studies 3–5 generalize the effect to other contexts and decisions and Studies 6–8 indicate that prospective outcome bias causes it (rather than regret aversion, waste aversion, goals-as-reference-points, probability weighting, or loss aversion). Finally, in Study 9, we find evidence for another prediction of prospective outcome bias: people prefer small increases in the probability of large rewards (e.g., a 1% improvement in their chances of winning $100) to large increases in the probability of small rewards (e.g., a 10% improvement in their chances of winning $10).

Joachim Vosgerau, Uri Simonsohn, Leif D. Nelson, Joseph Simmons (2019), 99% Impossible: A Valid, or Falsifiable, Internal Meta-Analysis, Journal of Experimental Psychology: General, 148 (), pp. 1628-1639.

Abstract: Several researchers have relied on, or advocated for, internal meta-analysis, which involves statistically aggregating multiple studies in a paper to assess their overall evidential value. Advocates of internal meta-analysis argue that it provides an efficient approach to increasing statistical power and solving the file-drawer problem. Here we show that the validity of internal-meta-analysis rests on the assumption that no studies or analyses were selectively reported. That is, the technique is only valid if (1) all conducted studies were included (i.e., an empty file-drawer), and (2) for each included study, exactly one analysis was attempted (i.e., there was no p-hacking). We show that even very small doses of selective reporting invalidate internal-meta-analysis. For example, the kind of minimal p-hacking that increases the false-positive rate of one study to just 8% increases the false-positive rate of a 10-study internal meta-analysis to 83%. If selective reporting is approximately zero, but not exactly zero, then internal meta-analysis is invalid. To be valid, (1) an internal meta-analysis would need to exclusively contain studies that were properly pre-registered, (2) those pre-registrations would have to be followed in all essential aspects, and (3) the decision of whether to include a given study in an internal meta-analysis would have to be made before any of those studies are run.