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A practical guide to making good decisions in a world of missing dataIn the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don't see.Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don’t know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions.Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones.

Big data. --- Data sets, Large --- Large data sets --- Data sets --- Missing observations (Statistics). --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Accuracy and precision. --- Adverse selection. --- Ambiguity. --- Analogy. --- Anonymity. --- Approximation. --- Arithmetic mean. --- Astronomer. --- Autism. --- Average. --- Awareness. --- Bankruptcy. --- Benford's law. --- Blood pressure. --- Calculation. --- Clinical trial. --- Confidentiality. --- Confirmation bias. --- Credit card. --- Credit score. --- Crime statistics. --- Customer. --- Dark data. --- Data science. --- Data set. --- Database. --- Decision-making. --- Detection. --- Disease. --- Economics. --- Effectiveness. --- Estimation. --- Experimental psychology. --- Explanation. --- Extrapolation. --- Financial transaction. --- Floor effect. --- Fraud. --- General Data Protection Regulation. --- Hypothesis. --- Identifier. --- Identity theft. --- Illustration. --- Imputation (statistics). --- Income. --- Indication (medicine). --- Inference. --- Information asymmetry. --- Insider trading. --- Insurance fraud. --- Insurance. --- Investor. --- Lawsuit. --- Measurement. --- Meta-analysis. --- Misinformation. --- Mismatch. --- Missing data. --- Observational error. --- Obstacle. --- Percentage. --- Plagiarism. --- Prediction. --- Probability. --- Proportionality (mathematics). --- Pseudonymization. --- Quantity. --- Randomized controlled trial. --- Randomness. --- Respondent. --- Result. --- Rounding. --- Salary. --- Sample Size. --- Sampling (statistics). --- Science. --- Scientist. --- Significant figures. --- Simulation. --- Skewness. --- Social science. --- Statistic. --- Statistical hypothesis testing. --- Statistician. --- Statistics. --- Suggestion. --- Symptom. --- Synthetic data. --- Tax avoidance. --- Tax. --- Technology. --- Theory. --- Thought. --- Uncertainty. --- Unemployment. --- Variable (mathematics). --- Wealth. --- Website. --- Writing. --- Year. --- Missing observations (Statistics)

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This book is about the function and use of official statistics. It welcomes the aspiration for official statistics to be an indispensable element in the information system of a democratic society, serving the government, the economy and the public with data about the economic, demographic, social and environmental situation. The book identifies the political role of official statisticians, who decided what gets measured as well as how it is measured. While thousands of official statistics are published every year, and some are quoted by politicians, used by policy-makers or reported in the media, the authors observe that, in the main, official statistics do not feature much in everyday lives of people and businesses. The book concludes with suggestions for more that should be done, especially in the context of improving wellbeing and helping meet the worldwide set of sustainable development goals set for 2030. Paul Allin is a Visiting Professor at Imperial College London, UK, researching the use of wellbeing measures. David Hand is Emeritus Professor of Mathematics and a Senior Research Investigator at Imperial College London, UK, where he previously chaired the Statistics Section.

Public policy. --- Social sciences—Philosophy. --- Public Policy. --- Social Philosophy. --- Social sciences --- Human comfort. --- Comfort, Human --- Creature comfort, Human --- Senses and sensation --- Human engineering --- Quality of life --- Social philosophy --- Social theory --- Philosophy.

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Analyse factorielle --- Factor analysis --- Factor analysis. --- Multivariate analysis - Graphic methods --- Statistique mathematique --- Analyse multivariee --- Methodes graphiques --- Multivariate analysis --- Statistics --- Analyse factorielle. --- Analyse multivariée. --- Statistique --- Graphic methods. --- Méthodes graphiques.

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An introduction to statistics and probability for students across a wide range of disciplines. The emphasis on problem solving through analysis of data is enhanced by extensive use of real data sets throughout, drawn from a wide range of subject areas to highlight the diversity of statistics.

Mathematical statistics --- Statistique mathématique --- #SBIB:309H510 --- #SBIB:303H510 --- Verbale communicatie: algemene werken --- Methoden sociale wetenschappen: statistische technieken, algemeen --- Mathematical statistics. --- Statistics --- Statistics. --- Statistique mathématique