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This paper discusses some aspects of selecting and testing random and pseudorandom number generators. The outputs of such generators may be used in many cryptographic applications, such as the generation of key material. Generators suitable for use in cryptographic applications may need to meet stronger requirements than for other applications. In particular, their outputs must be unpredictable in the absence of knowledge of the inputs. Some criteria for characterizing and selecting appropriate generators are discussed in this document. The subject of statistical testing and its relation to cryptanalysis is also discussed, and some recommended statistical tests are provided. These tests may be useful as a first step in determining whether or not a generator is suitable for a particular cryptographic application. However, no set of statistical tests can absolutely certify a generator as appropriate for usage in a particular application, i.e., statistical testing cannot serve as a substitute for cryptanalysis. The design and cryptanalysis of generators is outside the scope of this paper.

Random number generators. --- Statistical hypothesis testing. --- Computer security. --- Data encryption (Computer science) --- Hypothesis test --- P-value --- Random number generator --- Statistical tests

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Many scientists now widely agree that the current paradigm of statistical significance should be abandoned or largely modified. In response to these calls for change, a Special Issue of Econometrics (MDPI) has been proposed. This book is a collection of the articles that have been published in this Special Issue. These seven articles add new insights to the problem and propose new methods that lay a solid foundation for the new paradigm for statistical significance.

p-value --- Bayesian --- model specification --- model testing --- reporting results (p-values) --- replications --- equivalence --- minimum-effect --- non-inferiority --- point-null hypothesis testing --- zero probability paradox --- t-statistic --- pretest estimator --- model averaging --- a priori procedure --- null hypothesis significance testing --- confidence intervals --- p-values --- estimation --- hypothesis testing --- replication crisis --- profit maximization --- market failure --- teaching of econometrics --- regression analysis --- economics pedagogy --- n/a

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A PDF version of this book is available for free in open access via the OAPEN Library platform, www.oapen.org. This book examines the emergence of both financial markets and carbon markets, and provides an in-depth investigation on the fundamental determinants of financial development.

Macroeconomics. --- Urban economics. --- Banks and banking. --- Environmental economics. --- Development economics. --- Political economy. --- Macroeconomics/Monetary Economics//Financial Economics. --- Urban Economics. --- Banking. --- Environmental Economics. --- Development Economics. --- International Political Economy. --- Social sciences --- Economic man --- Economic theory --- Political economy --- Finance --- Financial institutions --- Money --- Agricultural banks --- Banking --- Banking industry --- Commercial banks --- Depository institutions --- Economics --- Cities and towns --- City economics --- Economics of cities --- Environmental quality --- Economic development --- Economic aspects --- Environmental aspects --- reform --- financiele markten --- carbon --- development --- financial markets --- koolstof --- ontwikkeling --- hervorming --- crisis --- Democracy --- Dependent and independent variables --- Estimator --- Gross domestic product --- Long run and short run --- P-value

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Chris Chambers draws on his own experiences as a working scientist to reveal a dark side to psychology that few of us ever see. Using the seven deadly sins as a metaphor, he shows how practitioners are vulnerable to powerful biases that undercut the scientific method, how they routinely torture data until it produces outcomes that can be published in prestigious journals, and how studies are much less reliable than advertised. He reveals how a culture of secrecy denies the public and other researchers access to the results of psychology experiments, how fraudulent academics can operate with impunity, and how an obsession with bean counting creates perverse incentives for academics ... Outlining a core set of best practices that can be applied across the sciences, Chambers demonstrates how all these sins can be corrected by embracing open science, an emerging philosophy that seeks to make research and its outcomes as transparent as possible. Provided by publisher. Psychological science has made extraordinary discoveries about the human mind, but can we trust everything its practitioners are telling us? In recent years, it has become increasingly apparent that a lot of research in psychology is based on weak evidence, questionable practices, and sometimes even fraud. The Seven Deadly Sins of Psychology diagnoses the ills besetting the discipline today and proposes sensible, practical solutions to ensure that it remains a legitimate and reliable science in the years ahead. In this unflinchingly candid manifesto, Chris Chambers draws on his own experiences as a working scientist to reveal a dark side to psychology that few of us ever see. Using the seven deadly sins as a metaphor, he shows how practitioners are vulnerable to powerful biases that undercut the scientific method, how they routinely torture data until it produces outcomes that can be published in prestigious journals, and how studies are much less reliable than advertised. He reveals how a culture of secrecy denies the public and other researchers access to the results of psychology experiments, how fraudulent academics can operate with impunity, and how an obsession with bean counting creates perverse incentives for academics. Left unchecked, these problems threaten the very future of psychology as a science but help is here. Outlining a core set of best practices that can be applied across the sciences, Chambers demonstrates how all these sins can be corrected by embracing open science, an emerging philosophy that seeks to make research and its outcomes as transparent as possible. Publisher's description

Psychology --- Psychologie --- Research --- Methodology --- Recherche --- Méthodologie --- Psychology, Clinical. --- Methodology. --- Research. --- Philosophy of science --- Méthodologie --- Recherche. --- Méthodologie. --- Psychological research --- Psychology, Clinical --- Academic publishing. --- Adversarial collaboration. --- Alzheimer's disease. --- Ambiguity. --- American Psychological Association. --- Article processing charge. --- Author. --- Bayes' theorem. --- Bayesian. --- Blog. --- Calculation. --- Career. --- Center for Open Science. --- Cherry picking. --- Cognitive psychology. --- Confirmation bias. --- Counting. --- Criticism. --- Data set. --- Data. --- Edition (book). --- Editorial. --- Effect size. --- Estimation. --- Experiment. --- Experimental psychology. --- Explanation. --- Fallacy. --- False positive rate. --- Finding. --- Fraud. --- Funding. --- Guideline. --- Hypothetico-deductive model. --- Impact factor. --- Independent scientist. --- Institution. --- Jargon. --- John Bargh. --- Law of small numbers. --- Literature. --- Manuscript. --- Meta-analysis. --- Misconduct. --- Narrative. --- Null hypothesis. --- Open science. --- P-value. --- PLOS ONE. --- PLOS. --- Paperback. --- Participant. --- Paywall. --- Peer review. --- Percentage. --- Post hoc analysis. --- Postdoctoral researcher. --- Precognition. --- Prevalence. --- Probability. --- Psychiatry. --- Psychological Science. --- Psychological research. --- Psychologist. --- Psychology. --- Psychonomic Society. --- Publication bias. --- Publication. --- Publishing. --- Quantity. --- Raw data. --- Reprimand. --- Reproducibility. --- Reputation. --- Requirement. --- Result. --- Reuse. --- Sample Size. --- Sampling (statistics). --- Science. --- Scientific literature. --- Scientific method. --- Scientific misconduct. --- Scientist. --- Scrutiny (journal). --- Scrutiny. --- Sharing. --- Signature. --- Social psychology. --- Statistical hypothesis testing. --- Statistical power. --- Statistical significance. --- Statistician. --- Statistics. --- Suggestion. --- Tilburg University. --- Type I and type II errors. --- Whistleblower. --- Writing. --- Méthodologie.

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Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointe.

Econometric models. --- Econometrics. --- Accuracy and precision. --- Asymptotic distribution. --- Autocorrelation. --- Autoregressive conditional heteroskedasticity. --- Autoregressive model. --- Bayesian statistics. --- Bayesian. --- Bernoulli distribution. --- Bias of an estimator. --- Calculation. --- Central limit theorem. --- Chow test. --- Cointegration. --- Conditional expectation. --- Conditional probability distribution. --- Confidence interval. --- Confidence region. --- Correlation and dependence. --- Correlogram. --- Count data. --- Cross-sectional data. --- Cross-sectional regression. --- Distribution function. --- Dummy variable (statistics). --- Econometric model. --- Empirical distribution function. --- Equation. --- Error term. --- Estimation. --- Estimator. --- Exogeny. --- Exploratory data analysis. --- F-distribution. --- F-test. --- Fair coin. --- Forecast error. --- Forecasting. --- Granger causality. --- Heteroscedasticity. --- Inference. --- Instrumental variable. --- Joint probability distribution. --- Law of large numbers. --- Least absolute deviations. --- Least squares. --- Likelihood function. --- Likelihood-ratio test. --- Linear regression. --- Logistic regression. --- Lucas critique. --- Marginal distribution. --- Markov process. --- Mathematical optimization. --- Maximum likelihood estimation. --- Model selection. --- Monte Carlo method. --- Moving-average model. --- Multiple correlation. --- Multivariate normal distribution. --- Nonparametric regression. --- Normal distribution. --- Normality test. --- One-Tailed Test. --- Opportunity cost. --- Orthogonalization. --- P-value. --- Parameter. --- Partial correlation. --- Poisson regression. --- Probability. --- Probit model. --- Quantile. --- Quantity. --- Quasi-likelihood. --- Random variable. --- Regression analysis. --- Residual sum of squares. --- Round-off error. --- Seemingly unrelated regressions. --- Selection bias. --- Simple linear regression. --- Skewness. --- Standard deviation. --- Standard error. --- Stationary process. --- Statistic. --- Student's t-test. --- Sufficient statistic. --- Summary statistics. --- T-statistic. --- Test statistic. --- Time series. --- Type I and type II errors. --- Unit root test. --- Unit root. --- Utility. --- Variable (mathematics). --- Variance. --- Vector autoregression. --- White test.