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About 10,000 papers for “statistical errors”

BMJ statistical errors

Oxford University Research Archive (ORA) (University of Oxford) · 2004

EDITOR—Abbasi in his Editor's choice discusses a study that found statistical errors in 25% of papers published by the BMJ in 2001.1 As statistical advisers to the BMJ we aim to improve the quality of published papers by ensuring that their conclusions are consistent with the data. To this end we hope to identify important errors that affect the interpretation of the findings, but care less about more minor errors. Any stricter policy would be impossibly time consuming. That said, we recognise that important errors do slip through from time to time, and are always keen to improve our performance. The particular errors flagged in the paper2 were inconsistencies between test statistics and P values. Out of 63 tests seven (11%) were wrong (for example 2 on 1 df = 4.2, P reported = 0.024, P actual = 0.0404). Yet in no case did the error affect the test's interpretation as to whether or not the results could have arisen by chance. This supports our belief that more extreme errors are likely to be weeded out at the review stage. The paper is disappointing in focusing on P values and by implication hypothesis testing. By contrast the BMJ's policy is to present the main findings as confidence intervals where the emphasis is on estimation.3

Cited by 0

BMJ statistical errors

Cole, Tim J, Altman, Douglas, Ashby, Deborah et al. · UCL Discovery (University College London) · 2004

EDITOR—Abbasi in his Editor's choice discusses a study that found statistical errors in 25% of papers published by the BMJ in 2001.1 As statistical advisers to the BMJ we aim to improve the quality of published papers by ensuring that their conclusions are consistent with the data. To this end we hope to identify important errors that affect the interpretation of the findings, but care less about more minor errors. Any stricter policy would be impossibly time consuming. That said, we recognise that important errors do slip through from time to time, and are always keen to improve our performance. \n\nThe particular errors flagged in the paper2 were inconsistencies between test statistics and P values. Out of 63 tests seven (11%) were wrong (for example 2 on 1 df = 4.2, P reported = 0.024, P actual = 0.0404). Yet in no case did the error affect the test's interpretation as to whether or not the results could have arisen by chance. This supports our belief that more extreme errors are likely to be weeded out at the review stage. The paper is disappointing in focusing on P values and by implication hypothesis testing. By contrast the BMJ's policy is to present the main findings as confidence intervals where the emphasis is on estimation.3

Cited by 0Open access

Nested sampling statistical errors

Andrew Fowlie, Li Qiao, Huifang Lv et al. · Monthly Notices of the Royal Astronomical Society · 2023

ABSTRACT Nested sampling (NS) is a popular algorithm for Bayesian computation. We investigate statistical errors in NS both analytically and numerically. We show two analytic results. First, we show that the leading terms in Skilling’s expression using information theory match the leading terms in Keeton’s expression from an analysis of moments. This approximate agreement was previously only known numerically and was somewhat mysterious. Second, we show that the uncertainty in single NS runs approximately equals the standard deviation in repeated NS runs. While intuitive, this was previously taken for granted. We close by investigating our results and their assumptions in several numerical examples, including cases in which NS uncertainties increase without bound.

Cited by 0Open access

Statistical errors in software

Miquel Porta, Ferrán Sanz · International Journal of Epidemiology · 1988

Journal Article Statistical errors in software Get access MIQUEL PORTA, MIQUEL PORTA Institute of Medical Research (IMIM), Universitat Autonomy de BarcelonaPg Maritim 25–29, E-08003 Barcelona Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar FERRAN SANZ FERRAN SANZ Institute of Medical Research (IMIM), Universitat Autonomy de BarcelonaPg Maritim 25–29, E-08003 Barcelona Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar International Journal of Epidemiology, Volume 17, Issue 4, December 1988, Page 931, https://doi.org/10.1093/ije/17.4.931 Published: 01 December 1988

Cited by 1

Errors in Statistical Programming

Douglas B. Johnson, Fei Ye, Anna K. Dewan · JAMA Oncology · 2019

titled "Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer," published online February 2, 2017, and also in the September 2017 issue of JAMA Oncology, 1 an error occurred in the statistical programming of the analysis. All analyses were recalculated, which resulted in changes to the population-attributable risk proportion estimates for some of the risk factors and combinations of risk factors, 2 most of which did not alter the conclusions. This article was corrected online.

Cited by 0Open access

Statistical errors / Polysynthetic languages

Sascha Wolfer, Alexander Koplenig · OSF Preprints (OSF Preprints) · 2025

Code and results for "Statistical errors undermine claims about the evolution of polysynthetic languages"

Cited by 0Open access

Comparison of Modern Statistical Errors and Indigenous Errors

Yash Srivastava, Sheela Misra, Amarjeet Singh · International Journal of Science and Research (IJSR) · 2025

For ages, the reliability and interpretation of data have been influenced by statistical errors, both ancient and contemporary. Indigenous systems, especially those from ancient India, provide distinctive methods of knowledge and measurement, whereas modern statistical procedures are based on formal methodologies. This study contrasts the epistemic and observational errors present in ancient Indian systems with the type of errors in contemporary statistics, with a particular emphasis on sampling and non-sampling errors. This paper aims comparative study between modern and ancient Indian error systems.

Cited by 0Open access

Statistical Prediction of Programming Errors.

R. W. Motley, W.Douglas Brooks · 1977

Abstract : This report presents and discusses the results obtained for statistical predictions of programming errors using multiple linear regression analysis. Programming errors were predicted from linear combinations of program characteristics and programmer variables. Each of the program characteristics variables were considered to be measures of the program's complexity and structure. Two distinct data samples comprising 783 programs with approximately 297,000 source instructions written for command and control software applications were analyzed. Background data on both samples is provided which includes discussions related to each sample's software development environment, testing conditions, predictor variables, definition of programming errors, and general data characteristics. Results are presented which give the prediction equations obtained and a discussion of the predictability of errors and error rate in each sample. Conclusion of the study and recommendations for further research are also provided. (Author)

Cited by 29

Dimensions, errors and statistical analysis

Gareth Rees · Cambridge University Press eBooks · 1994

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Cited by 0

Common Errors in Statistical Analyses

Rovetta, Alessandro · Figshare · 2021

This short technical report addresses various statistical topics that have often been objects of misconception. In particular, it will be provided sufficient mathematical proofs and counterexamples to resolve some recurrent interpretative errors on the central limit theorem, confidence intervals, correlations, and P-values.

Cited by 2Open access

Common Errors in Statistical Analyses

Rovetta, Alessandro · Figshare · 2021

This short technical report addresses various statistical topics that have often been objects of misconception. In particular, it will be provided sufficient mathematical proofs and counterexamples to resolve some recurrent interpretative errors on the central limit theorem, confidence intervals, correlations, and P-values.

Cited by 0Open access

Statistical Errors in Advanced Estimates

Wiley series in probability and statistics · 2010

This chapter discusses statistical errors in advanced parameter estimates. It emphasizes on frequency domain properties of joint sample records from two different stationary (ergodic) random processes. The advanced parameter estimates discussed in the chapter include magnitude and phase estimates of cross-spectral density functions, followed by various quantities contained in single-input/ output problems and multiple-input/output problems. In particular, statistical error formulas are developed for frequency response function estimates (gain and phase), coherence function estimates, coherent output spectrum estimates, multiple coherence function estimates, and partial coherence function estimates. The chapter presents summaries on the main normalized random error formulas for various spectral density estimates and single-input/output model estimates. Controlled Vocabulary Terms coherence; cross spectrum; frequency domain; input/output process; random error; spectral density function; statistics

Cited by 3