This volume focuses on the abuse of statistical inference in scientific and statistical literature, as well as in a variety of other sources, presenting examples of misused statistics to show that many scientists and statisticians are unaware of, or unwilling to challenge the chaotic state of statistical practices.; The book: provides examples of ubiquitous statistical tests taken from the biomedical and behavioural sciences, economics and the statistical literature; discusses conflicting views of randomization, emphasizing certain aspects of induction and epistemology; reveals fallacious…mehr
This volume focuses on the abuse of statistical inference in scientific and statistical literature, as well as in a variety of other sources, presenting examples of misused statistics to show that many scientists and statisticians are unaware of, or unwilling to challenge the chaotic state of statistical practices.; The book: provides examples of ubiquitous statistical tests taken from the biomedical and behavioural sciences, economics and the statistical literature; discusses conflicting views of randomization, emphasizing certain aspects of induction and epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as instant formulas to draw causal relationships; treats constructive uses of statistics, such as a modern version of Fisher's puzzle, Bayesian analysis, Shewhart control chart, descriptive statistics, chi-square test, nonlinear modeling, spectral estimation and Markov processes in quality control.
Part 1 Fads and fallacies in hypothesis testing: examples the t test; a two stage test of significance; more examples a Kolmogorov Smirnov test; mechanical application of statistical tests; data snooping; an appreciation of non significant results; type I and type II errors for decision making; type I and type II errors for general scientists. Part 2 Quasi inferential statistics: randomness or chaos? Hume's problem; unobservables, semi unobservables and grab sets; is statistics a science?; grab sets and quasi inferential statistics; concluding remarks quasi and pseudo inferential statistics. Part 3 Statistical causality and law like relationships: sense and nonsense in causal inference examples; Rubin's model and controlled experiments; Rubin's model and observational studies; causal inference in sample survey and other observational studies; causes, indicators and latent variables. Part 4 Amoeba regression and time series models: discovering causal structure science now can be easily cloned; regression and time series analysis science or lunacy? (part I); regression and time series analysis science or lunacy? (part II); regression and time series analysis science or lunacy? (part III); statistical correlation versus physical causation. Part 5 A critical eye and an appreciative mind toward subjective knowledge: the sorry state of statistical evaluation a case study in educational research; modeling interaction effects a case study from the social behavioural sciences. Part 6 On objectivity, subjectivity and probability: statistical justification of scientific knowledge and scientific philosophy; classical probability, common sense and a strange view of nature; intuition and subjective knowledge in action the Bayes theorem (and its misuse); Bayesian time series analysis and E. T. (extra time series) judgment; a pursuit of information beyond the data in randomized and nonrandomized studies; women and love a case study in qualitative/quantitative analysis. Part 7 A delicate balance between order and chaos: reliability; order within chaos heartbeats, brainwaves, simulated annealing and the fiddling of a system. Part 8 The riddle of the ubiquitous statistics: information and misinformation; statistical quality control and the conflicting teachings of Q.C. Gurus. Epilogue toward a new perspective on statistical inference.
Part 1 Fads and fallacies in hypothesis testing: examples the t test; a two stage test of significance; more examples a Kolmogorov Smirnov test; mechanical application of statistical tests; data snooping; an appreciation of non significant results; type I and type II errors for decision making; type I and type II errors for general scientists. Part 2 Quasi inferential statistics: randomness or chaos? Hume's problem; unobservables, semi unobservables and grab sets; is statistics a science?; grab sets and quasi inferential statistics; concluding remarks quasi and pseudo inferential statistics. Part 3 Statistical causality and law like relationships: sense and nonsense in causal inference examples; Rubin's model and controlled experiments; Rubin's model and observational studies; causal inference in sample survey and other observational studies; causes, indicators and latent variables. Part 4 Amoeba regression and time series models: discovering causal structure science now can be easily cloned; regression and time series analysis science or lunacy? (part I); regression and time series analysis science or lunacy? (part II); regression and time series analysis science or lunacy? (part III); statistical correlation versus physical causation. Part 5 A critical eye and an appreciative mind toward subjective knowledge: the sorry state of statistical evaluation a case study in educational research; modeling interaction effects a case study from the social behavioural sciences. Part 6 On objectivity, subjectivity and probability: statistical justification of scientific knowledge and scientific philosophy; classical probability, common sense and a strange view of nature; intuition and subjective knowledge in action the Bayes theorem (and its misuse); Bayesian time series analysis and E. T. (extra time series) judgment; a pursuit of information beyond the data in randomized and nonrandomized studies; women and love a case study in qualitative/quantitative analysis. Part 7 A delicate balance between order and chaos: reliability; order within chaos heartbeats, brainwaves, simulated annealing and the fiddling of a system. Part 8 The riddle of the ubiquitous statistics: information and misinformation; statistical quality control and the conflicting teachings of Q.C. Gurus. Epilogue toward a new perspective on statistical inference.
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