To assess the threat of experimenter demand, we ask whether a hypothetical "ill-intentioned" researcher can manipulate inference. Four classic behavioral comparative statics are evaluated, and the potential for false inference is gauged by differentially applying strong positive and negative experimenter demand across the relevant decision pair. Evaluating three different subject pools (laboratory, Prolific, and MTurk) we find no evidence of experimenter demand eliminating or reversing directional effects. The response to experimenter demand is very limited for all three subject pools and is not large enough to generate false negatives, though we do find evidence of false positives when testing precise nulls in larger online-subject pools.

Excuse-seeking behavior that facilitates replacing altruistic choices with selfinterested ones has been documented in several domains. In a laboratory study, we replicate three leading papers on this topic: Dana et al. (2007), and the use of information avoidance; Exley (2015), and the use of differential risk preferences; and Di Tella et al. (2015), and the use of motivated beliefs. The replications were conducted as part of a graduate course, attempting to embed one answer to the growing call for experimental replications within the pedagogic process. We fully replicate the simpler Dana et al. paper, and broadly replicate the core findings for the other two projects, though with reduced effect sizes and a failure to replicate on some secondary measures. Finally, we attempt to connect behaviors to facilitate the understanding of how each fit within the broader literature. However, we find no connections across domains.

We examine the experimenter's preferences over different populations using statistical power under a fixed budget as the stand-in for the researcher's utility. We consider five populations commonly used in experiments by economists: undergraduate students at a physical location, undergraduate students in a virtual setting, Amazon MTurk "workers", a filtered MTurk subset from CloudResearch, and Prolific. Focusing on noise due to inattention, observation costs dominate the comparisons, with the larger online population samples superior to the smaller lab samples. However, once we factor in responsiveness to treatment, the lab samples have greater power than either MTurk or Prolific.

I develop a novel model of group-based communication in which group members communicate with one another. Communication is costly in the sense that group members who choose to send or listen to messages incur costs. Equilibrium strategies have an intuitive characterization - those with the best information send, those with the worst information receive. Free-riding leads to less information exchange than is optimal, but a simple system of transfers and subsidies can correct this. Examining the model's predictions with an experiment I find that subjects over-communicate when costs are high, but fail to benefit from this as much as they should. Additionally, I find that listening costs are more harmful to fare, in contrast with the theory which indicates sending costs.

We outline a mechanism for eliciting probabilities using two uniform random numbers that is equivalent to the binarized scoring rule (BSR). Though our implementation is simple to describe and has a non-mathematical explanation, it retains the desirable theoretical features of the BSR. Moreover, we show that a discretized version with evenly-spaced reporting intervals can be implemented in the field with no more equipment than a pair of dice.