Author
Michael Kirchler
Other affiliations: University of Gothenburg
Bio: Michael Kirchler is an academic researcher from University of Innsbruck. The author has contributed to research in topics: Experimental finance & Financial market. The author has an hindex of 33, co-authored 118 publications receiving 6791 citations. Previous affiliations of Michael Kirchler include University of Gothenburg.
Papers published on a yearly basis
Papers
More filters
University of Southern California1, Duke University2, Stockholm School of Economics3, Center for Open Science4, University of Virginia5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, Research Institute of Industrial Economics11, New York University12, Cardiff University13, Mathematica Policy Research14, Northwestern University15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.
1,586 citations
Posted Content•
TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.
Abstract: We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.
1,415 citations
TL;DR: To contribute data about replicability in economics, 18 studies published in the American Economic Review and the Quarterly Journal of Economics between 2011 and 2014 are replicated, finding that two-thirds of the 18 studies examined yielded replicable estimates of effect size and direction.
Abstract: The replicability of some scientific findings has recently been called into question. To contribute data about replicability in economics, we replicated 18 studies published in the American Economic Review and the Quarterly Journal of Economics between 2011 and 2014. All of these replications followed predefined analysis plans that were made publicly available beforehand, and they all have a statistical power of at least 90% to detect the original effect size at the 5% significance level. We found a significant effect in the same direction as in the original study for 11 replications (61%); on average, the replicated effect size is 66% of the original. The replicability rate varies between 67% and 78% for four additional replicability indicators, including a prediction market measure of peer beliefs.
811 citations
California Institute of Technology1, Stockholm School of Economics2, University of Innsbruck3, National University of Singapore4, University of Pennsylvania5, University of Virginia6, Center for Open Science7, Harvard University8, Ludwig Maximilian University of Munich9, University of Amsterdam10, Harbin Institute of Technology11
TL;DR: It is found that peer beliefs of replicability are strongly related to replicable, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.
Abstract: Being able to replicate scientific findings is crucial for scientific progress. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 2015. The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies. We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators. Consistent with these results, the estimated true-positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility. Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.
759 citations
Rotem Botvinik-Nezer1, Rotem Botvinik-Nezer2, Felix Holzmeister3, Colin F. Camerer4 +217 more•Institutions (78)
TL;DR: The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
Abstract: Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
551 citations
Cited by
More filters
01 Jan 1982
Abstract: Introduction 1. Woman's Place in Man's Life Cycle 2. Images of Relationship 3. Concepts of Self and Morality 4. Crisis and Transition 5. Women's Rights and Women's Judgment 6. Visions of Maturity References Index of Study Participants General Index
7,539 citations
Journal Article•
TL;DR: Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of the authors' brain’s wiring.
Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.
4,351 citations
[...]
01 Jan 1998
TL;DR: The four Visegrad states (Poland, Czech Republic, Slovakia and Hungary) form a compact area between Germany and Austria in the west and the states of the former USSR in the east as discussed by the authors.
Abstract: The four Visegrad states — Poland, the Czech Republic, Slovakia (until 1993 Czechoslovakia) and Hungary — form a compact area between Germany and Austria in the west and the states of the former USSR in the east. They are bounded by the Baltic in the north and the Danube river in the south. They are cut by the Sudeten and Carpathian mountain ranges, which divide Poland off from the other states. Poland is an extension of the North European plain and like the latter is drained by rivers that flow from south to north west — the Oder, the Vlatava and the Elbe, the Vistula and the Bug. The Danube is the great exception, flowing from its source eastward, turning through two 90-degree turns to end up in the Black Sea, forming the barrier and often the political frontier between central Europe and the Balkans. Hungary to the east of the Danube is also an open plain. The region is historically and culturally part of western Europe, but its eastern Marches now represents a vital strategic zone between Germany and the core of the European Union to the west and the Russian zone to the east.
3,056 citations
University of Southern California1, Duke University2, Stockholm School of Economics3, Center for Open Science4, University of Virginia5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, Research Institute of Industrial Economics11, New York University12, Cardiff University13, Mathematica Policy Research14, Northwestern University15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.
1,586 citations
Posted Content•
TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.
Abstract: We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.
1,415 citations