74 research outputs found
The fallacy of placing confidence in confidence intervals
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; CIs are thought to be a guide to which parameter values are plausible or reasonable; 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. We show in a number of examples that CIs do not necessarily have any of these properties, and can lead to unjustified or arbitrary inferences. For this reason, we caution against relying upon confidence interval theory to justify interval estimates, and suggest that other theories of interval estimation should be used instead
Psychometric properties of the Nursing Home Survey on Patient Safety Culture in Norwegian nursing homes
“It Must Be Me”: Ethnic Diversity and Attributions for Peer Victimization in Middle School
The measurement of media literacy in eating disorder risk factor research: psychometric properties of six measures
Tweaking the entrepreneurial orientation–performance relationship in family firms: the effect of control mechanisms and family-related goals
Adrenaline increases the rate of cycling of crossbridges in rat cardiac muscle as measured by pseudo-random binary noise-modulated perturbation analysis.
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