Intransitive cycles: Rational choice or random error? An answer based on estimation of error rates with experimental data

1993 ◽  
Vol 35 (3) ◽  
pp. 311-336 ◽  
Author(s):  
Barry Sopher ◽  
Gary Gigliotti
Keyword(s):  
Experimental Data ◽  
Rational Choice ◽  
Random Error ◽  
Error Rates ◽  
Estimation Of Error

scholarly journals Statistical model specification and power: recommendations on the use of test-qualified pooling in analysis of experimental data

2017 ◽  
Vol 284 (1851) ◽  
pp. 20161850 ◽  
Author(s):  
Nick Colegrave ◽  
Graeme D. Ruxton
Keyword(s):  
Experimental Data ◽  
Statistical Model ◽  
Statistical Power ◽  
Error Term ◽  
Degrees Of Freedom ◽  
Type I Error ◽  
Error Rates ◽  
Statistical Testing ◽  
Model Specification ◽  
Type I

A common approach to the analysis of experimental data across much of the biological sciences is test-qualified pooling. Here non-significant terms are dropped from a statistical model, effectively pooling the variation associated with each removed term with the error term used to test hypotheses (or estimate effect sizes). This pooling is only carried out if statistical testing on the basis of applying that data to a previous more complicated model provides motivation for this model simplification; hence the pooling is test-qualified. In pooling, the researcher increases the degrees of freedom of the error term with the aim of increasing statistical power to test their hypotheses of interest. Despite this approach being widely adopted and explicitly recommended by some of the most widely cited statistical textbooks aimed at biologists, here we argue that (except in highly specialized circumstances that we can identify) the hoped-for improvement in statistical power will be small or non-existent, and there is likely to be much reduced reliability of the statistical procedures through deviation of type I error rates from nominal levels. We thus call for greatly reduced use of test-qualified pooling across experimental biology, more careful justification of any use that continues, and a different philosophy for initial selection of statistical models in the light of this change in procedure.

Real-Time Data Logging and Online Curve Fitting Using Raspberry Pi in Physics Laboratories

2020 ◽  
Vol 18 (3) ◽  
pp. 57-77
Author(s):  
Wing-Kwong Wong ◽  
Kai-Ping Chen ◽  
Jia-Wei Lin
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Curve Fitting ◽  
Empirical Studies ◽  
Error Rates ◽  
Raspberry Pi ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Physics Laboratories

The results of PISA 2015 indicate that Taiwanese students have excellent mathematical and scientific knowledge but are weak in applying such knowledge and in conducting practical experiments in the laboratory. To support students conducting practical experiments in physics laboratories, a real-time data logging system and an online tool for fitting experimental data were developed. During data logging in an experiment, the data was immediately plotted, which enabled students to observe the characteristics of the plot. The online curve fitting system, which employed Internet of Things technologies, allowed students to fit experimental data to various mathematical functions and plot a function curve superimposed on the data. Two empirical studies were conducted involving first-year university students and secondary school teachers. The results indicated that these developed tools improved students' understanding of an experiment's mathematical characteristics. The average curve fitting error rates of students and teachers were 4.62% and 1.4%, respectively.

Estimation of Error-rates in Classification Rules

1992 ◽  
pp. 548-552
Author(s):  
R. J. Henery ◽  
R. King ◽  
A. Sutherland ◽  
J. M. O. Mitchell ◽  
P. Brazdil
Keyword(s):  
Error Rates ◽  
Classification Rules ◽  
Estimation Of Error

THE DETERMINATION OF ENERGY BY AB INITIO METHODS: ESTIMATION OF ERRORS AND SOME IMPROVEMENTS

2007 ◽  
Vol 06 (02) ◽  
pp. 269-279 ◽  
Author(s):  
ABRAHAM F. JALBOUT
Keyword(s):  
Experimental Data ◽  
Statistical Analysis ◽  
Ab Initio ◽  
Random Error ◽  
Theoretical Calculations ◽  
Ab Initio Methods ◽  
Accurate Data ◽  
Standard Procedures ◽  
Random Components

This work presents a statistical analysis of errors in the ab initio determination of molecular energy. These sets of analyses have allowed us to separate the errors in systematic and random components and also to realize that differences between experimental data and theoretical calculations are larger than those initially suspected. Although there is a limit to how small this difference can be analyzed by our methods, procedures to improve ab initio molecular energies are proposed. This has been achieved by reducing the systematic error obtained by correlating the calculated results to the most accurate data (in this case CCSD (T)), as well as by reducing the random error by mixing the results of different standard procedures.

Bias in estimation of error in change-over trials with dairy cattle

1951 ◽  
Vol 41 (1-2) ◽  
pp. 146-148 ◽  
Author(s):  
H. L. Lucas
Keyword(s):  
Experimental Data ◽  
Dairy Cattle ◽  
Latin Square ◽  
Usual Method ◽  
Direct Effects ◽  
Estimation Of Error ◽  
Carry Over

A study has been made of the bias in error which occurs when Latin-square change-over trials conducted on dairy cattle are analysed by the usual method for Latin-square experiments, with modification for carry-over effects. Bias is present for adjusted direct effects and for permanent effects (direct plus carry-over), but does not exist for unadjusted direct effects.Analyses of fifteen sets of experimental data showed that the bias is of no importance in 3 × 3 designs but might be serious for some practical situations in the 4 × 4 designs. A tentative factor to correct for bias was given for the latter case.

Estimation of Error Rates in Several-Population Discriminant Analysis

1982 ◽  
Vol 19 (1) ◽  
pp. 57-61 ◽  
Author(s):  
Stephen C. Hora ◽  
James B. Wilcox
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Posterior Probability ◽  
Error Rates ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Probability Estimator ◽  
Estimation Of Error

Researchers seeking to estimate the classification accuracy of linear discriminant functions in a more than two-population setting have had little guidance as to the most appropriate technique. The authors review the available techniques and present an additional alternative which combines features of the U-method and the recently developed posterior probability estimator. The new alternative is compared with other methods by Monté Carlo simulation.

Sequential multilocation auditing and the New York food stamps program

2018 ◽  
Author(s):  
Karl W. Heiner ◽  
Marc Kennedy ◽  
Anthony O'Hagan
Keyword(s):  
New York ◽  
Hierarchical Model ◽  
Bayesian Methods ◽  
Bayesian Model ◽  
Error Rates ◽  
Food Stamps ◽  
Estimation Of Error ◽  
One Year ◽  
Over Time

This article discusses the use of Bayesian methods in analysing data that evolve over time in sequential multilocation auditing. Using the New York food stamps program as a case study, it proposes a model that incorporates a nonparametric component for the error magnitudes (taints), a hierarchical model for overall error rates across counties and parameters controlling the variation of rates from one year to the next, including an overall trend in error rates. The article first provides an overview of the New York food stamps program, along with the auditing concepts and terminology, before introducing the Bayesian model. This model is used to examine a sample of individual awards of food stamps to see if the value awarded is correct according to the rules of the scheme. The model makes it possible to smooth estimation of error rates and error classes in small counties across counties and through time.

scholarly journals An evaluation of ways of using equilibrium dialysis to quantify the binding of ligand to macromolecule

1977 ◽  
Vol 165 (1) ◽  
pp. 107-110 ◽  
Author(s):  
I A Nimmo ◽  
G L Atkins ◽  
R C Strange ◽  
I W Percy-Robb
Keyword(s):  
Experimental Data ◽  
Systematic Error ◽  
Random Error ◽  
Reliable Method ◽  
Equilibrium Dialysis ◽  
Simulated Data ◽  
Experimental Designs ◽  
Ligand Complex ◽  
Binding Isotherm ◽  
Error Loss

1. The effect of systematic error (loss of ligand, complex or macromolecule) on three of the experimental designs by which equilibrium dialysis may be used to quantify the interaction of ligand and macromolecule is examined theoretically, and the design that is least sensitive to systematic error is identified. 2. Thirteen methods for fitting the binding isotherm to experimental data are compared by using them to analyse simulated data containing random error, and the most reliable method is identified.

Sign in / Sign up

Export Citation Format

Share Document