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Applied Power Analysis for the Behavioral Sciences is a practical "how-to" guide to conducting statistical power analyses for psychology and related fields. The book provides a guide to conducting analyses that is appropriate for researchers and students, including those with limited quantitative backgrounds. With practical use in mind, the text provides detailed coverage of topics such as how to estimate expected effect sizes and power analyses for complex designs. The topical coverage of the text, an applied approach, in-depth coverage of popular statistical procedures, and a focus on conducting analyses using R make the text a unique contribution to the power literature. To facilitate application and usability, the text includes ready-to-use R code developed for the text. An accompanying R package called pwr2ppl (available at https://github.com/chrisaberson/pwr2ppl) provides tools for conducting power analyses across each topic covered in the text.

Social sciences - Statistical methods --- Statistical power analysis --- Social sciences --- Statistical power analysis. --- Statistical methods. --- Power analysis (Statistics) --- Statistical hypothesis testing

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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.