Richard Morey on the "The fallacy of placing confidence in confidence intervals"

Talk at the University of Sussex May 2016

Slides available here: http://learnbayes.org/talks/UniSussex...

Abstract: Interval estimates -- estimates of parameters that include an allowance for sampling uncertainty -- have long been touted as a key component of statistical analyses. There are several kinds of interval estimates, but the most popular are confidence intervals (CIs): intervals that contain the true parameter value in some known proportion of repeated samples, on average. The width of confidence intervals is thought to index the precision of an estimate; the parameter values contained within a CI are thought to be more plausible than those outside the interval; and the confidence coefficient of the interval (e.g. 95%) is thought to index the plausibility that the true parameter is included in the interval. CIs do not necessarily have any of these properties; given that confidence intervals do not necessarily have these desirable properties, their use may lead to unjustified or arbitrary inferences if researchers rely on them. For this reason, we caution against the use of confidence interval theory to justify interval estimates.

1. confidence interval vs prediction interval, confidence interval equation, confidence interval for mean, significance testing exercises chapters 22-23, bayesian statistics from concept to data analysis, confidence interval example, bayesian statistics in r, significance testing python, significance testing in spss, bayesian credibility interval example, 95 credibility interval, bayesian statistics columbia, credibility interval wiki, significance testing in tableau, bayesian statistics example, credibility interval calculator, confidence interval for proportion, credibility interval surveys, confidence interval 95, confidence interval chart, bayesian statistics an introduction, significance testing, bayesian statistics book, credibility interval r, bayesian statistics applications, bayesian statistics explained, confidence interval for population proportion, confidence interval for difference in means, significance testing is based on which of the following principles, bayesian statistics khan academy, confidence interval statistics, significance testing practice problems, confidence interval calculator, confidence intervals explained, confidence interval for population mean, confidence interval formula, credibility interval vs margin of error, bayesian statistics the fun way, significance testing in psychology, bayesian statistics python, ipsos credibility interval, significance testing with population data, credibility interval interpretation, credibility interval binomial, bayesian credibility interval binomial, significance testing proportions, bayesian statistics gatech, bayesian statistics pdf, confidence interval excel, confidence interval definition, significance testing at 95 confidence, bayesian statistics course, 90 credibility interval, significance testing linear regression, significance testing chi square, confidence interval ti 84, bayesian statistics youtube, credibility interval distribution, posterior credibility interval, significance testing in r, credibility interval meta analysis, confidence interval table, , bayesian statistics using stan, bayesian statistics and marketing, significance testing in excel, significance testing p value, credibility interval poll, significance testing definition, significance testing calculator, credibility interval vs confidence interval, bayesian statistics for hackers, confidence interval in r, bayesian statistics coursera, significance testing for means calculator, bayesian statistics machine learning, significance testing exercises, confidence interval interpretation

Richard Morey on the "The fallacy of placing confidence in confidence intervals"
5 Likes	5 Dislikes
381 views views	followers
People & Blogs	Upload TimePublished on 23 May 2016