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An important and influential stream of research in cognitive psychology has been the study of human reasoning under uncertainty, and especially the study of cognitive illusions of logical and statistical inference. Of particular importance for methodologists are Kahneman and Tversky's investigations of a fallacious belief in the "Law of Small Numbers" (Tversky and Kahneman, 1971), since a substantive implication is that much of the experimental work using orthodox statistical significance testing is of little interest, due to a common lack statistical power. In an interesting thread of this research, Thomas Gilovich and Amos Tversky famously claimed that the belief in the "Hot Hand" in basketball is a cognitive illusion, an instance of the common, but erroneous, belief in the Law of Small Numbers (Gilovich et al, 1985;Tversky and Gilovich, 1989). They supported this claim with a variety of statistical tests failing to show any clear evidence of a Hot Hand phenomenon. This idea that belief in the Hot Hand is illusory has since been very widely promulgated in the cognitive sciences and in popularizations, for example by Stephen Jay Gould in Bully for Brontosaurus (Gould, 1991) and by Gilovich himself in his How We Know What Isn't So (Gilovich, 1991). The curious fact is, however, that these cognitive psychologists themselves employed orthodox statistical significance tests, without performing any power analysis. Here we show that their belief in having demonstrated the illusory status of the Hot Hand is itself an illustration of the Law of Small Numbers, for their statistical tests were of such low power that they could not have been expected to find a Hot Hand even if it were present.

Background: While technology for treating traumatic brain injury (TBI) continues to improve, unexplained variation in outcomes persists. A growing body of literature provides evidence for the influence of subjective experience on objective physiological processes. We worked with TBI survivors and their family/caregivers to develop a model of recovery that incorporates patients’ subjective experience with objective measures of functional status (FS) to investigate factors that foster recovery. We then developed a measure of FS that reflects the priorities and perspectives of the patient population. The purpose of this report is to present our process for establishing the reliability and validity of the FS measure.Methods: Through an iterative process we elicited from TBI survivors their priorities for evaluation and measurement of FS. We assembled their responses into an 18-item measure and administered the pilot survey to 68 people with TBI from support groups in Oregon. Final administration was a survey mailed to 837 TBI survivors. Reliability analyses were conducted to evaluate internal consistency. A Cronbach’s Alpha was calculated for each sub-construct of the scale. A Difficulty Function was calculated to compare patient responses to family responses within each construct. Content validity analysis was conducted comparing constructs and items from the measure to those of 11 established, validated TBI outcome measures. an Analysis of Variance (ANOVA) was conducted to investigate the relationship between survivors’ FS scores and their post-trauma socio-economic status.Results: There were 248 complete cases in the sample. Reliability analysis provided adequate inter-item correlations. Difficulty functions ranged from .69 to .47. Construct validity was established with 11 validated instruments used to evaluate TBI. The ANOVA revealed a significant difference between FS scores and post-trauma SES; survivors with lower FS scores experienced lower post-trauma SES.Conclusions: Our goal was to develop a measure of functional status following TBI using the advice and assistance of survivors of TBI and their families. In this paper we present our process for developing and assessing the reliability and validity of the Functional Status measure of the Bob Brown Scale. Future research will be use of the BBS in a prospective sample of TBI survivors.

In multi-tenant environments, Linux containers managed by Docker or Kubernetes have a lower resource footprint, faster startup times, and higher I/O performance com- pared to virtual machines (VMs) on hypervisors. Yet their weaker isolation guarantees, enforced through soft- ware kernel mechanisms, make it easier for attackers to compromise the confidentiality and integrity of applica- tion data within containers. We describe SCONE, a secure container mechanism for Docker that uses the SGX trusted execution support of Intel CPUs to protect container processes from out- side attacks. The design of SCONE leads to (i) a small trusted computing base (TCB) and (ii) a low performance overhead: SCONE offers a secure C standard library in- terface that transparently encrypts/decrypts I/O data; to reduce the performance impact of thread synchronization and system calls within SGX enclaves, SCONE supports user-level threading and asynchronous system calls. Our evaluation shows that it protects unmodified applications with SGX, achieving 0.6✓–1.2✓ of native throughput.