Is There Selection Bias in Laboratory Experiments? The Case of Social and Risk Preferences

Laboratory experiments are frequently used to examine the nature of individual preferences and inform economic theory. However, it is unknown whether the preferences of volunteer participants are representative of the population from which the participants are drawn or whether they differ due to selection bias. We examine whether the social and risk preferences of participants in a laboratory experiment represent the preferences of the population from which they are recruited. To answer this question, we measured the preferences of 1,173 students in a classroom experiment. Separately, we invited all students to participate in a laboratory experiment. We find that the social and risk preferences of students who attend the laboratory experiment do not differ significantly from the preferences of the population from which they were recruited. Moreover, participation decisions based on social and risk preferences do not differ significantly across most subgroups, with the exception that female participants are on average less risk averse than female non-participants, and male participants are more risk averse than male non-participants.selection bias, laboratory experiments, external validity, social preferences, risk preferences

Is There Selection Bias in Laboratory Experiments?*

Do the social and risk preferences of participants in laboratory experiments represent the preferences of the population from which they are recruited? To answer this question, we conducted a classroom experiment with a population of 1,173 students using a trust game and a lottery choice task to measure individual preferences. Separately, all 1,173 students were invited to participate in a laboratory experiment. To determine whether selection bias exists, we compare the preferences of the individuals who eventually participated in a laboratory experiment to those in the population. We find that the social and risk preferences of the students participating in the laboratory experiment are not significantly different from the preferences of the population from which they were recruited. We further show that participation decisions across most subgroups (e.g., men vs. women) do not differ significantly. We therefore fail to find selection bias based on social and risk preferences.external validity; social preferences; selection bias; laboratory experiments; risk preferences

Is There Selection Bias in Laboratory Experiments?*

Do the social and risk preferences of participants in laboratory experiments represent the preferences of the population from which they are recruited? To answer this question, we conducted a classroom experiment with a population of 1,173 students using a trust game and a lottery choice task to measure individual preferences. Separately, all 1,173 students were invited to participate in a laboratory experiment. To determine whether selection bias exists, we compare the preferences of the individuals who eventually participated in a laboratory experiment to those in the population. We find that the social and risk preferences of the students participating in the laboratory experiment are not significantly different from the preferences of the population from which they were recruited. We further show that participation decisions across most subgroups (e.g., men vs. women) do not differ significantly. We therefore fail to find selection bias based on social and risk preferences

The Fragility and Robustness of Trust

Although it is well known that trust is an important component of the fulfilment of incomplete contracts, less is known regarding how robust it is to past experiences. We present an experiment in which trust is required for transactions to occur, and nature provides a series of shocks along the path of play. Although the shocks have a short-term impact, we find that trust is surprisingly robust in the long-term. We argue that trust, through fragile in one way, is in another way more robust and stable over time than previously known. The results shed light on the resilence of economic institutions with incomplete contracts.Trust, Repeated Games, Experimental Economics

Inferring Repeated Game Strategies From Actions: Evidence From Trust Game Experiments

This paper is an empirical study, using new experimental data, of repeated game strategies in trust games; its goal is to identify strategies that people use in repeated games. We develop a strategy inference method that maps observed actions to a set of best fitting unobserved repeated game strategies. Data analysis shows the ability of the method to infer distinct but intuitive and theoretically justified sets of strategies across finitely and indefinitely repeated games. In indefinitely repeated trust games we infer trigger strategies that are consistent with equilibria. In finitely repeated games we infer strategies with end-game effects. Almost all strategies inferred are best responses to the inferred strategies of opponents. For the first time we hypothesize repeated game strategies based on observed behavior, and characterize observed behavior using the core game theory concept of repeated-game strategies.Game Theory, Empirical Methods, Experimental Economics, Repeated Games, Trust

Savings and Prize-Linked Savings Accounts

Many households have insufficient savings to handle moderate and routine consumption shocks. Many of these financially-fragile households also have the highest lottery expenditures as a proportion of income. This combination suggests that Prize-Linked Savings (PLS) accounts, combining security of principal with lottery-type jackpots, can increase savings among these at-risk households. Results from an online experiment show that the introduction of PLS accounts increase total savings and reduce lottery expenditures significantly, especially among individuals with the lowest levels of savings and income. The results imply that PLS accounts offer a plausible market-based solution to encourage individuals to increase savings

Pompe disease diagnosis and management guideline

ACMG standards and guidelines are designed primarily as an educational resource for physicians and other health care providers to help them provide quality medical genetic services. Adherence to these standards and guidelines does not necessarily ensure a successful medical outcome. These standards and guidelines should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed to obtaining the same results. in determining the propriety of any specific procedure or test, the geneticist should apply his or her own professional judgment to the specific clinical circumstances presented by the individual patient or specimen. It may be prudent, however, to document in the patient's record the rationale for any significant deviation from these standards and guidelines.Duke Univ, Med Ctr, Durham, NC 27706 USAOregon Hlth Sci Univ, Portland, OR 97201 USANYU, Sch Med, New York, NY USAUniv Florida, Coll Med, Powell Gene Therapy Ctr, Gainesville, FL 32611 USAIndiana Univ, Bloomington, in 47405 USAUniv Miami, Miller Sch Med, Coral Gables, FL 33124 USAHarvard Univ, Childrens Hosp, Sch Med, Cambridge, MA 02138 USAUniversidade Federal de São Paulo, São Paulo, BrazilColumbia Univ, New York, NY 10027 USANYU, Bellevue Hosp, Sch Med, New York, NY USAColumbia Univ, Med Ctr, New York, NY 10027 USAUniversidade Federal de São Paulo, São Paulo, BrazilWeb of Scienc

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice