Andrew Gelman
Biographic Data
| ID | 298096 |
|---|---|
| NAME | Andrew Gelman |
| GIVEN NAMES | Andrew |
| FAMILY NAME | Gelman |
| SIGNATURE | GELMAN A |
| AFFILIATIONS | Columbia University |
| ORCID | 0000-0002-6975-2601 |
| VERIFIED | Yes |
| TOTAL WORKS | 93 |
| TOTAL CITATIONS | 1646 |
| AUTHOR COUNT | 91 |
| EDITOR COUNT | 2 |
| FIRST PUBLICATION YEAR | 1990 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 18 |
Adjusting for Underreporting of Child Protective Services Involvement in the Future of Families and Child Wellbeing Study and Assessing Its Empirical Implications Through Illustrative Analyses of Youn…
Child protective services (CPS) involvement is common among American families; more than a third of all children and more than half of Black children experience at least one investigation by age 18. However, studying the causes and consequences of CPS involvement is challenging. National data on children involved with CPS lack a counterfactual group, and state-level administrative data often miss key family and child factors. The Future of Famili…
Normative scientific conflict is unavoidable and should be welcomed: 26 Jul 2025
Interrogating the 'cargo cult science' metaphor
Who Wants School Vouchers in America? A Comprehensive Study Using Multilevel Regression and Poststratification
The debate surrounding school vouchers in educational policy remains contentious, with conflicting survey data presenting contradictory levels of public endorsement. To gain a more nuanced comprehension of viewpoints towards vouchers, we propose deconstructing the American populace into distinct demographic and geographical sectors. However, this approach encounters challenges due to data insufficiency arising from small sample sizes in individua…
The Great Society, Reagan's Revolution, and Generations of Presidential Voting
We build a model of American presidential voting in which the cumulative impression left by political events determines the preferences of voters. The impression varies by voter, depending on their age at the time the events took place. We use the Gallup presidential approval‐rating time series to reflect the major events that influence voter preferences, with the most influential occurring during a voter's teenage and early adult years. Our fitt…
Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)
Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic Rˆ of Gelman and Rubin (1992) has serious flaws. Traditional Rˆ will fail to correctly diagnose convergence failures when the chain has a heavy tail or when the variance varies across the chains. In this paper we propose an alte…
How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis
The use of statistical inference in linguistics and related areas like psychology typically involves a binary decision: either reject or accept some null hypothesis using statistical significance testing. When statistical power is low, this frequentist data-analytic approach breaks down: null results are uninformative, and effect size estimates associated with significant results are overestimated. Using an example from psycholinguistics, several…
Research on registered report research
Discussion points for Bayesian inference
Voter Registration Databases and MRP: Toward the Use of Large-Scale Databases in Public Opinion Research
Declining telephone response rates have forced several transformations in survey methodology, including cell phone supplements, nonprobability sampling, and increased reliance on model-based inferences. At the same time, advances in statistical methods and vast amounts of new data sources suggest that new methods can combat some of these problems. We focus on one type of data source—voter registration databases—and show how they can improve infer…
Abandon Statistical Significance
We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm—and the p-value thresholds intrinsi…
R-squared for Bayesian Regression Models
The usual definition of R2 (variance of the predicted values divided by the variance of the data) has a problem for Bayesian fits, as the numerator can be larger than the denominator. We propose an alternative definition similar to one that has appeared in the survival analysis literature: the variance of the predicted values divided by the variance of predicted values plus the expected variance of the errors.
Visualization in Bayesian Workflow
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from …
A consensus-based transparency checklist
We present a consensus-based checklist to improve and document the transparency of research reports in social and behavioural research. An accompanying online application allows users to complete the form and generate a report that they can submit with their manuscript or post to a public repository
Author Correction: A consensus-based transparency checklist
An amendment to this paper has been published and can be accessed via a link at the top of the paper
Benefits and limitations of randomized controlled trials: A commentary on Deaton and Cartwright
How to Think Scientifically about Scientists’ Proposals for Fixing Science
Gaydar and the Fallacy of Decontextualized Measurement
Recent media coverage of studies about 'gaydar,' the supposed ability to detect another's sexual orientation through visual cues, reveal problems in which the ideals of scientific precision strip the context from intrinsically social phenomena. This fallacy of objective measurement, as we term it, leads to nonsensical claims based on the predictive accuracy of statistical significance. We interrogate these gaydar studies' assumption that there is…
Stan: A Probabilistic Programming Language
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood …
Practical Bayesian model evaluation using leave-one-out cross-validation and Waic
Measurement error and the replication crisis
The assumption that measurement error always reduces effect sizes is false
The Prior Can Often Only Be Understood in the Context of the Likelihood
A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys’ priors, reference priors, maximum entropy priors, and weakly informative priors. These methods, however, often manifest a key conceptual tension in prior modeling: a model encoding true prior information should be chosen without reference to the model of the measurement process, bu…
Increasing Transparency Through a Multiverse Analysis
Empirical research inevitably includes constructing a data set by processing raw data into a form ready for statistical analysis. Data processing often involves choices among several reasonable options for excluding, transforming, and coding data. We suggest that instead of performing only one analysis, researchers could perform a multiverse analysis, which involves performing all analyses across the whole set of alternatively processed data sets…
The Mythical Swing Voter
Most surveys conducted during the 2012 U.S. presidential campaign showed large swings in support for the Democratic and Republican candidates, especially before and after the first presidential debate. Using a combination of traditional cross-sectional surveys, a unique panel survey (in terms of scale, frequency, and source), and a high response rate panel, we find that daily sample composition varied more in response to campaign events than did …
Hierarchical Models for Causal Effects
Hierarchical models play three important roles in modeling causal effects: (i) accounting for data collection, such as in stratified and split‐plot experimental designs; (ii) adjusting for unmeasured covariates, such as in panel studies; and (iii) capturing treatment effect variation, such as in subgroup analyses. Across all three areas, hierarchical models, especially Bayesian hierarchical modeling, offer substantial benefits over classical, non…
Partisans without Constraint: Political Polarization and Trends in American Public Opinion
Public opinion polarization is here conceived as a process of alignment along multiple lines of potential disagreement and measured as growing constraint in individuals' preferences. Using NES data from 1972 to 2004, the authors model trends in issue partisanship—the correlation of issue attitudes with party identification—and issue alignment—the correlation between pairs of issues—and find a substantive increase in issue partisanship, but little…
Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable
As most political scientists know, the outcome of the American presidential election can be predicted within a few percentage points (in the popular vote), based on information available months before the election. Thus, the general campaign for president seems irrelevant to the outcome (except in very close elections), despite all the media coverage of campaign strategy. However, it is also well known that the pre-election opinion polls can vary…
Estimating Incumbency Advantage without Bias
In this paper we prove theoretically and demonstrate empirically that all existing measures of incumbency advantage in the congressional elections literature are biased or inconsistent. We then provide an unbiased estimator based on a very simple linear regression model. We apply this new method to congressional elections since 1900, providing the first evidence of a positive incumbency advantage in the first half of the century
Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls
We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997,Survey Methodology23:127–135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the…
Enhancing Democracy Through Legislative Redistricting
We demonstrate the surprising benefits of legislative redistricting (including partisan gerrymandering) for American representative democracy. In so doing, our analysis resolves two long-standing controversies in American politics. First, whereas some scholars believe that redistricting reduces electoral responsiveness by protecting incumbents, others, that the relationship is spurious, we demonstrate that both sides are wrong: redistricting incr…
Segregation in Social Networks Based on Acquaintanceship and Trust
Using 2006 General Social Survey data, the authors compare levels of segregation by race and along other dimensions of potential social cleavage in the contemporary United States. Americans are not as isolated as the most extreme recent estimates suggest. However, hopes that "bridging" social capital is more common in broader acquaintanceship networks than in core networks are not supported. Instead, the entire acquaintanceship network is perceiv…
Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation
Logistic regression models have been used in political science for estimating ideal points of legislators and Supreme Court justices. These models present estimation and identifiability challenges, such as improper variance estimates, scale and translation invariance, reflection invariance, and issues with outliers. We address these issues using Bayesian hierarchical modeling, linear transformations, informative regression predictors, and explici…
A Unified Method of Evaluating Electoral Systems and Redistricting Plans
We derive a unified statistical method with which one can produce substantially improved definitions and estimates of almost any feature of two-party electoral systems that can be defined based on district vote shares. Our single method enables one to calculate more efficient estimates, with more trustworthy assessments of their uncertainty, than each of the separate multifarious existing measures of partisan bias, electoral responsiveness, seats…
Voting as a Rational Choice: Why and How People Vote To Improve the Well-Being of Others
For voters with `social' preferences, the expected utility of voting is approximately independent of the size of the electorate, suggesting that rational voter turnouts can be substantial even in large elections. Less important elections are predicted to have lower turnout, but a feedback mechanism keeps turnout at a reasonable level under a wide range of conditions. The main contributions of this paper are: (1) to show how, for an individual wit…
Systemic Consequences of Incumbency Advantage in U.S. House Elections
The dramatic increase in the electoral advantage of incumbency has sparked widespread interest among congressional researchers over the last 15 years. Although many scholars have studied the advantages of incumbency for incumbents, few have analyzed its effects on the underlying electoral system. We examine the influence of the incumbency advantage on two features of the electoral system in U.S. House elections: electoral responsiveness and parti…
Rich State, Poor State, Red State, Blue State: What’s the Matter with Connecticut
For decades, the Democrats have been viewed as the party of the poor, with the Republicans representing the rich. Recent presidential elections, however, have shown a reverse pattern, with Democrats performing well in the richer blue states in the northeast and coasts, and Republicans dominating in the red states in the middle of the country and the south. Through multilevel modeling ofindividual-level survey data and county- and state-level demo…
Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups
Using multilevel regression and poststratification (MRP), we estimate voter turnout and vote choice within deeply interacted subgroups: subsets of the population that are defined by multiple demographic and geographic characteristics. This article lays out the models and statistical procedures we use, along with the steps required to fit the model for the 2004 and 2008 presidential elections. Though MRP is an increasingly popular method, we impro…
Economic Disparities and Life Satisfaction in European Regions
Standard Voting Power Indexes Do Not Work: An Empirical Analysis
Voting power indexes such as that of Banzhaf are derived, explicitly or implicitly, from the assumption that all votes are equally likely (i.e., random voting). That assumption implies that the probability of a vote being decisive in a jurisdiction with n voters is proportional to 1/√ n . In this article the authors show how this hypothesis has been empirically tested and rejected using data from various US and European elections. They find that …
The Mythical Swing Voter
Most surveys conducted during the 2012 U.S. presidential campaign showed large swings in support for the Democratic and Republican candidates, especially before and after the first presidential debate. Using a combination of traditional cross-sectional surveys, a unique panel survey (in terms of scale, frequency, and source), and a high response rate panel, we find that daily sample composition varied more in response to campaign events than did …
Two-Stage Regression and Multilevel Modeling: A Commentary
These articles demonstrate, in several different examples, the effectiveness of two-level regression: the procedure of fitting several separate regression models, and then fitting a second, higher-level, regression to the estimated coefficients (for example, fitting a separate regression model to survey data from each of several countries, then regressing the coefficient estimates on country-level predictors). For simplicity, we will refer to the…
Average Predictive Comparisons for Models with Nonlinearity, Interactions, and Variance Components
In a predictive model, what is the expected difference in the outcome associated with a unit difference in one of the inputs? In a linear regression model without interactions, this average predictive comparison is simply a regression coefficient (with associated uncertainty). In a model with nonlinearity or interactions, however, the average predictive comparison in general depends on the values of the predictors. We consider various definitions…
The Great Society, Reagan's Revolution, and Generations of Presidential Voting
We build a model of American presidential voting in which the cumulative impression left by political events determines the preferences of voters. The impression varies by voter, depending on their age at the time the events took place. We use the Gallup presidential approval‐rating time series to reflect the major events that influence voter preferences, with the most influential occurring during a voter's teenage and early adult years. Our fitt…
Income Inequality and Partisan Voting in the United States
Objectives. Income inequality in the United States has risen during the past several decades. Has this produced an increase in partisan voting differences between rich and poor? Methods. We examine trends from the 1940s through the 2000s in the country as a whole and in the states. Results. We find no clear relation between income inequality and class‐based voting. Conclusions. Factors such as religion and education result in a less clear pattern…
A Review: Preelection Survey Methodology: Details From Eight Polling Organizations, 1988 and 1992
Journal Article THE POLLS—A REVIEW: PREELECTION SURVEY METHODOLOGY: DETAILS FROM EIGHT POLLING ORGANIZATIONS, 1988 AND 1992 Get access STEPHEN VOSS, STEPHEN VOSS D. STEPHEN VOSS is a doctoral candidate in the Department of Government of Harvard University. ANDREW GELMAN is assistant professor in the Department of Statistics in the University of California, Berkeley. GARY KING is professor in the Department of Government of Harvard University. The…
Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches
We consider the relative performance of two common approaches to multiple imputation (MI): joint multivariate normal (MVN) MI, in which the data are modeled as a sample from a joint MVN distribution; and conditional MI, in which each variable is modeled conditionally on all the others. In order to use the multivariate normal distribution, implementations of joint MVN MI typically assume that categories of discrete variables are probabilistically …
Polls and Elections Understanding Persuasion and Activation in Presidential Campaigns: The Random Walk and Mean Reversion Models
Political campaigns are commonly understood as random walks, during which, at any point in time, the level of support for any party or candidate is equally likely to go up or down. Each shift in the polls is then interpreted as the result of some combination of news and campaign strategies. A completely different story of campaigns is the mean reversion model in which the elections are determined by fundamental factors of the economy and partisan…
Avoiding Model Selection in Bayesian Social Research
Introduction Raftery's paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and predictions when there are not enough data available to fit the desired model using standard techniques. For both problems, we agree with Raftery that classical frequentist methods fail …
Bayesian Combination of State Polls and Election Forecasts
A wide range of potentially useful data are available for election forecasting: the results of previous elections, a multitude of preelection polls, and predictors such as measures of national and statewide economic performance. How accurate are different forecasts? We estimate predictive uncertainty via analysis of data collected from past elections (actual outcomes, preelection polls, and model estimates). With these estimated uncertainties, we…
Voter Registration Databases and MRP: Toward the Use of Large-Scale Databases in Public Opinion Research
Declining telephone response rates have forced several transformations in survey methodology, including cell phone supplements, nonprobability sampling, and increased reliance on model-based inferences. At the same time, advances in statistical methods and vast amounts of new data sources suggest that new methods can combat some of these problems. We focus on one type of data source—voter registration databases—and show how they can improve infer…
Estimating Incumbency Advantage without Bias
In this paper we prove theoretically and demonstrate empirically that all existing measures of incumbency advantage in the congressional elections literature are biased or inconsistent. We then provide an unbiased estimator based on a very simple linear regression model. We apply this new method to congressional elections since 1900, providing the first evidence of a positive incumbency advantage in the first half of the century
Systemic Consequences of Incumbency Advantage in U.S. House Elections
The dramatic increase in the electoral advantage of incumbency has sparked widespread interest among congressional researchers over the last 15 years. Although many scholars have studied the advantages of incumbency for incumbents, few have analyzed its effects on the underlying electoral system. We examine the influence of the incumbency advantage on two features of the electoral system in U.S. House elections: electoral responsiveness and parti…
Inference from Iterative Simulation Using Multiple Sequences
The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rath…
Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable
As most political scientists know, the outcome of the American presidential election can be predicted within a few percentage points (in the popular vote), based on information available months before the election. Thus, the general campaign for president seems irrelevant to the outcome (except in very close elections), despite all the media coverage of campaign strategy. However, it is also well known that the pre-election opinion polls can vary…
A Unified Method of Evaluating Electoral Systems and Redistricting Plans
We derive a unified statistical method with which one can produce substantially improved definitions and estimates of almost any feature of two-party electoral systems that can be defined based on district vote shares. Our single method enables one to calculate more efficient estimates, with more trustworthy assessments of their uncertainty, than each of the separate multifarious existing measures of partisan bias, electoral responsiveness, seats…
Enhancing Democracy Through Legislative Redistricting
We demonstrate the surprising benefits of legislative redistricting (including partisan gerrymandering) for American representative democracy. In so doing, our analysis resolves two long-standing controversies in American politics. First, whereas some scholars believe that redistricting reduces electoral responsiveness by protecting incumbents, others, that the relationship is spurious, we demonstrate that both sides are wrong: redistricting incr…
A Review: Preelection Survey Methodology: Details From Eight Polling Organizations, 1988 and 1992
Journal Article THE POLLS—A REVIEW: PREELECTION SURVEY METHODOLOGY: DETAILS FROM EIGHT POLLING ORGANIZATIONS, 1988 AND 1992 Get access STEPHEN VOSS, STEPHEN VOSS D. STEPHEN VOSS is a doctoral candidate in the Department of Government of Harvard University. ANDREW GELMAN is assistant professor in the Department of Statistics in the University of California, Berkeley. GARY KING is professor in the Department of Government of Harvard University. The…
Handbook of Statistical Modeling for the Social and Behavioral Sciences
Avoiding Model Selection in Bayesian Social Research
Introduction Raftery's paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and predictions when there are not enough data available to fit the desired model using standard techniques. For both problems, we agree with Raftery that classical frequentist methods fail …
General Methods for Monitoring Convergence of Iterative Simulations
We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order to develop convergence-monitoring summaries that are relevant for the purposes for which the simulations are used. We recommend applying a battery of tests for…
Improving on Probability Weighting for Household Size
Journal Article Improving on Probability Weighting for Household Size Get access ANDREW GELMAN, ANDREW GELMAN Search for other works by this author on: Oxford Academic Google Scholar THOMAS C. LITTLE THOMAS C. LITTLE Search for other works by this author on: Oxford Academic Google Scholar Public Opinion Quarterly, Volume 62, Issue 3, November 1998, Pages 398–404, https://doi.org/10.1086/297852 Published: 01 November 1998
Evaluating and Using Statistical Methods in the Social Sciences: A Discussion of “A Critique of the Bayesian Information Criterion for Model Selection
Introduction The "Bayesian information criterion" (BIC) can be a helpful statistical tool in sociology and elsewhere (see Raftery, 1995, and Kass and Raftery, 1995, for discussion and examples). However, Weakliem (1998) presents several powerful criticisms, both theoretical and applied, of BIC, which are similar to critical issues discussed in Gelman and Rubin (1995). Weakliem's paper makes three main points. First, the "Bayesian information crit…
Bayesian Data Analysis
Regression Modeling and Meta-Analysis for Decision Making: A Cost-Benefit Analysis of Incentives in Telephone Surveys
Regression models are often used, explicitly or implicitly, for decision making. However, the choices made in setting up the models (e.g., inclusion of predictors based on statistical significance) do not map directly into decision procedures. Bayesian inference works more naturally with decision analysis but presents problems in practice when noninformative prior distributions are used with sparse data. We do not attempt to provide a general sol…
Applied Bayesian Modeling and Causal Inference from Incomplete‐Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family
Applied Bayesian modeling and causal inference from incomplete-data perspectives: An essential journey with Donald Rubin's statistical family
Standard Voting Power Indexes Do Not Work: An Empirical Analysis
Voting power indexes such as that of Banzhaf are derived, explicitly or implicitly, from the assumption that all votes are equally likely (i.e., random voting). That assumption implies that the probability of a vote being decisive in a jurisdiction with n voters is proportional to 1/√ n . In this article the authors show how this hypothesis has been empirically tested and rejected using data from various US and European elections. They find that …
Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls
We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997,Survey Methodology23:127–135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the…
A Simple Scheme to Improve the Efficiency of Referenda
Two-Stage Regression and Multilevel Modeling: A Commentary
These articles demonstrate, in several different examples, the effectiveness of two-level regression: the procedure of fitting several separate regression models, and then fitting a second, higher-level, regression to the estimated coefficients (for example, fitting a separate regression model to survey data from each of several countries, then regressing the coefficient estimates on country-level predictors). For simplicity, we will refer to the…
Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation
Logistic regression models have been used in political science for estimating ideal points of legislators and Supreme Court justices. These models present estimation and identifiability challenges, such as improper variance estimates, scale and translation invariance, reflection invariance, and issues with outliers. We address these issues using Bayesian hierarchical modeling, linear transformations, informative regression predictors, and explici…
Data Analysis Using Regression and Multilevel/Hierarchical Models
Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through s…
Multilevel (Hierarchical) Modeling: What It Can and Cannot Do
Multilevel (hierarchical) modeling is a generalization of linear and generalized linear modeling in which regression coefficients are themselves given a model, whose parameters are also estimated from data. We illustrate the strengths and limitations of multilevel modeling through an example of the prediction of home radon levels in U.S. counties. The multilevel model is highly effective for predictions at both levels of the model, but could easi…
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral-$t$ family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors in this family. We use an example to illustrate serious problems with the inverse-gamma family of "noninformative" prior distributions. We suggest in…
The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant
It is common to summarize statistical comparisons by declarations of statistical significance or nonsignificance. Here we discuss one problem with such declarations, namely that changes in statistical significance are often not themselves statistically significant. By this, we are not merely making the commonplace observation that any particular threshold is arbitrary—for example, only a small change is required to move an estimate from a 5.1% si…
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