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Journal articles on the topic 'Confidence intervals'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Perry, D. C., X. L. Griffin, M. Dritsaki, M. L. Costa, and N. Parsons. "Becoming confident about confidence intervals." Bone & Joint Journal 99-B, no. 5 (2017): 563–65. http://dx.doi.org/10.1302/0301-620x.99b5.bjj-2017-0075.

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2

Kahraman, Cengiz, Basar Oztaysi, and Sezi Cevik Onar. "Interval-Valued Intuitionistic Fuzzy Confidence Intervals." Journal of Intelligent Systems 28, no. 2 (2019): 307–19. http://dx.doi.org/10.1515/jisys-2017-0139.

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Abstract Confidence intervals are useful tools for statistical decision-making purposes. In case of incomplete and vague data, fuzzy confidence intervals can be used for decision making under uncertainty. In this paper, we develop interval-valued intuitionistic fuzzy (IVIF) confidence intervals for population mean, population proportion, differences in means of two populations, and differences in proportions of two populations. The developed IVIF intervals can be used in cases of both finite and infinite population sizes. The developed fuzzy confidence intervals are equivalent decision-making

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3

Darling, HS. "Are you confident about your confidence in confidence intervals?" Cancer Research, Statistics, and Treatment 5, no. 1 (2022): 139. http://dx.doi.org/10.4103/crst.crst_75_22.

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4

Columb, M. O., and H. E. Thomson. "Confidence with confidence intervals." British Journal of Anaesthesia 95, no. 1 (2005): 111–12. http://dx.doi.org/10.1093/bja/aei569.

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5

El-Masri, Maher M. "Confidence in confidence intervals!" Canadian Journal of Nursing Research 49, no. 1 (2017): 3–4. http://dx.doi.org/10.1177/0844562117692406.

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6

Tufanaru, Catalin. "Confidence in confidence intervals." JBI Database of Systematic Reviews and Implementation Reports 14, no. 5 (2016): 1–2. http://dx.doi.org/10.11124/jbisrir-2016-002923.

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7

Gordon, Ian. "Confidence intervals." Medical Journal of Australia 145, no. 7 (1986): 361–62. http://dx.doi.org/10.5694/j.1326-5377.1986.tb113858.x.

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8

BROWNER, WARREN S. "Confidence Intervals." Annals of Internal Medicine 105, no. 6 (1986): 973. http://dx.doi.org/10.7326/0003-4819-105-6-973_3.

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9

Twa, Michael D. "Confidence Intervals." Optometry and Vision Science 97, no. 5 (2020): 315. http://dx.doi.org/10.1097/opx.0000000000001527.

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10

Campbell, Malcolm. "Confidence intervals." African Journal of Midwifery and Women's Health 10, no. 3 (2016): 115–19. http://dx.doi.org/10.12968/ajmw.2016.10.3.115.

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11

Cumming, Geoff, and Fiona Fidler. "Confidence Intervals." Zeitschrift für Psychologie / Journal of Psychology 217, no. 1 (2009): 15–26. http://dx.doi.org/10.1027/0044-3409.217.1.15.

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Most questions across science call for quantitative answers, ideally, a single best estimate plus information about the precision of that estimate. A confidence interval (CI) expresses both efficiently. Early experimental psychologists sought quantitative answers, but for the last half century psychology has been dominated by the nonquantitative, dichotomous thinking of null hypothesis significance testing (NHST). The authors argue that psychology should rejoin mainstream science by asking better questions – those that demand quantitative answers – and using CIs to answer them. They explain CI

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12

Newman, G. B. "CONFIDENCE INTERVALS." Lancet 329, no. 8540 (1987): 1036. http://dx.doi.org/10.1016/s0140-6736(87)92307-5.

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13

Bulpitt, C. J. "CONFIDENCE INTERVALS." Lancet 329, no. 8531 (1987): 494–97. http://dx.doi.org/10.1016/s0140-6736(87)92100-3.

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14

Raftery, S. "Confidence intervals." Current Anaesthesia & Critical Care 10, no. 4 (1999): 222–23. http://dx.doi.org/10.1016/s0953-7112(99)90057-1.

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15

Makridakis, Spyros, Michèle Hibon, Ed Lusk, and Moncef Belhadjali. "Confidence intervals." International Journal of Forecasting 3, no. 3-4 (1987): 489–508. http://dx.doi.org/10.1016/0169-2070(87)90045-8.

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16

Daly, L. "Confidence intervals." BMJ 297, no. 6640 (1988): 66. http://dx.doi.org/10.1136/bmj.297.6640.66-c.

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17

Hatch, Maureen C. "Confidence intervals." Reproductive Toxicology 7, no. 4 (1993): 393. http://dx.doi.org/10.1016/0890-6238(93)90027-5.

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18

Short, S. "Confidence intervals." Journal of the American Veterinary Medical Association 197, no. 5 (1990): 576–78. http://dx.doi.org/10.2460/javma.1990.197.05.576.

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19

Bazarbayeva, Larissa, Madina Ostemirova, and Nuray Sarsenova. "CONFIDENCE INTERVALS FOR 5 MINUTES." Annali d'Italia 60 (October 25, 2024): 45–51. https://doi.org/10.5281/zenodo.13993290.

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Interval estimation is a fundamental concept in statistical inference, allowing researchers to construct confidence intervals for population parameters. This paper provides a comprehensive overview of the methods and formulas for constructing confidence intervals for various population parameters, including the mean of a normally distributed population, the population proportion, and the difference between means. The paper also discusses the importance of sample size determination for estimation and the use of different distributions, such as the standard normal distribution, Chi-square distri

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20

Wilcox, Rand R. "Confidence intervals for prediction intervals." Journal of Applied Statistics 33, no. 3 (2006): 317–26. http://dx.doi.org/10.1080/02664760500445962.

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21

Baudoin, Claude, and John O'quigley. "Symmetric Intervals and Confidence Intervals." Biometrical Journal 36, no. 8 (1994): 927–34. http://dx.doi.org/10.1002/bimj.4710360805.

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22

Schulte, Ann C., and Gary D. Borich. "False confidence in intervals: Inaccuracies in reporting confidence intervals." Psychology in the Schools 25, no. 4 (1988): 405–12. http://dx.doi.org/10.1002/1520-6807(198810)25:4<405::aid-pits2310250408>3.0.co;2-7.

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23

Schwertman, Neil C., and Larry R. Dion. "Quadratic Confidence Intervals." College Mathematics Journal 24, no. 5 (1993): 453. http://dx.doi.org/10.2307/2687019.

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24

Bofinger, Eve. "Expanded confidence intervals." Communications in Statistics - Theory and Methods 14, no. 8 (1985): 1849–64. http://dx.doi.org/10.1080/03610928508829017.

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25

Cadeddu, Margherita, Forough Farrokhyar, Carolyn Levis, Sylvie Cornacchi, Ted Haines, and Achilleas Thoma. "understanding confidence intervals." Canadian Journal of Surgery 55, no. 3 (2012): 207–11. http://dx.doi.org/10.1503/cjs.010211.

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26

Cole, Stephen R., and R. Cliff Blair. "Overlapping confidence intervals." Journal of the American Academy of Dermatology 41, no. 6 (1999): 1051–52. http://dx.doi.org/10.1016/s0190-9622(99)70281-1.

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27

Bland, Martin. "Misleading confidence intervals." Lancet 370, no. 9588 (2007): 653. http://dx.doi.org/10.1016/s0140-6736(07)61333-6.

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28

Gardner, MartinJ, and DouglasG Altman. "USING CONFIDENCE INTERVALS." Lancet 329, no. 8535 (1987): 746. http://dx.doi.org/10.1016/s0140-6736(87)90384-9.

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29

Schwertman, Neil C., and Larry R. Dion. "Quadratic Confidence Intervals." College Mathematics Journal 24, no. 5 (1993): 453–57. http://dx.doi.org/10.1080/07468342.1993.11973568.

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30

Smith, C. J. "Interpreting confidence intervals." Phlebology: The Journal of Venous Disease 27, no. 3 (2012): 141–42. http://dx.doi.org/10.1258/phleb.2012.012j02.

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31

Babu, G. J., and A. Bose. "Bootstrap confidence intervals." Statistics & Probability Letters 7, no. 2 (1988): 151–60. http://dx.doi.org/10.1016/0167-7152(88)90042-9.

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32

Sedgwick, P. "Understanding confidence intervals." BMJ 349, oct06 12 (2014): g6051. http://dx.doi.org/10.1136/bmj.g6051.

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33

Weerahandi, Samaradasa. "Generalized Confidence Intervals." Journal of the American Statistical Association 88, no. 423 (1993): 899–905. http://dx.doi.org/10.1080/01621459.1993.10476355.

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34

Kamper, Steven J. "Showing confidence (intervals)." Brazilian Journal of Physical Therapy 23, no. 4 (2019): 277–78. http://dx.doi.org/10.1016/j.bjpt.2019.01.003.

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35

Thompson, Bruce. "Effect sizes, confidence intervals, and confidence intervals for effect sizes." Psychology in the Schools 44, no. 5 (2007): 423–32. http://dx.doi.org/10.1002/pits.20234.

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36

Turek, Daniel. "Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals." Journal of Probability and Statistics 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/420483.

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Model averaging is a technique used to account for model uncertainty, in both Bayesian and frequentist multimodel inferences. In this paper, we compare the performance of model-averaged Bayesian credible intervals and frequentist confidence intervals. Frequentist intervals are constructed according to the model-averaged tail area (MATA) methodology. Differences between the Bayesian and frequentist methods are illustrated through an example involving cloud seeding. The coverage performance and interval width of each technique are then studied using simulation. A frequentist MATA interval perfor

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37

Kee, F., D. Gorman, and W. Odling-Smee. "Confidence intervals and interval cancers … needles and haystacks?" Public Health 106, no. 1 (1992): 29–35. http://dx.doi.org/10.1016/s0033-3506(05)80326-9.

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38

Severini, Thomas A. "Bayesian Interval Estimates Which are Also Confidence Intervals." Journal of the Royal Statistical Society: Series B (Methodological) 55, no. 2 (1993): 533–40. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01921.x.

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39

Schall, Robert. "The empirical coverage of confidence intervals: Point estimates and confidence intervals for confidence levels." Biometrical Journal 54, no. 4 (2012): 537–51. http://dx.doi.org/10.1002/bimj.201100134.

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40

LIM, JAE-HAK, SANG WOOK SHIN, DAE KYUNG KIM, and DONG HO PARK. "BOOTSTRAP CONFIDENCE INTERVALS FOR STEADY-STATE AVAILABILITY." Asia-Pacific Journal of Operational Research 21, no. 03 (2004): 407–19. http://dx.doi.org/10.1142/s021759590400031x.

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Steady-state availability, denoted by A, has been widely used as a measure to evaluate the reliability of a repairable system. In this paper, we develop new confidence intervals for steady-state availability based on four bootstrap methods; standard bootstrap confidence interval, percentile bootstrap confidence interval, bootstrap-t confidence interval, and bias-corrected and accelerated confidence interval. We also investigate the accuracy of these bootstrap confidence intervals by calculating the coverage probability and the average length of intervals.

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41

Abu-Shawiesh, Moustafa Omar Ahmed, and Aamid Saghir. "Robust Confidence Intervals for the Population Mean Alternatives to the Student-t Confidence Interval." Journal of Modern Applied Statistical Methods 18, no. 1 (2020): 2–20. http://dx.doi.org/10.22237/jmasm/1556669160.

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In this paper, three robust confidence intervals are proposed as alternatives to the Student t confidence interval. The performance of these intervals was compared through a simulation study shows that Qn-t confidence interval performs the best and it is as good as Student’s t confidence interval. Real-life data was used for illustration and performing a comparison that support the findings obtained from the simulation study.

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42

Visscher, Peter M., Robin Thompson, and Chris S. Haley. "Confidence Intervals in QTL Mapping by Bootstrapping." Genetics 143, no. 2 (1996): 1013–20. http://dx.doi.org/10.1093/genetics/143.2.1013.

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Abstract The determination of empirical confidence intervals for the location of quantitative trait loci (QTLs) was investigated using simulation. Empirical confidence intervals were calculated using a bootstrap resampling method for a backcross population derived from inbred lines. Sample sizes were either 200 or 500 individuals, and the QTL explained 1,5, or 10% of the phenotypic variance. The method worked well in that the proportion of empirical confidence intervals that contained the simulated QTL was close to expectation. In general, the confidence intervals were slightly conservatively

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43

Botella, Juan, and Julio Sánchez-Meca. "Meta-analysis: Confidence intervals and Prediction intervals." Anales de Psicología 40, no. 2 (2024): 344–54. http://dx.doi.org/10.6018/analesps.591831.

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Several types of intervals are usually employed in meta-analysis, a fact that has generated some confusion when interpreting them. Confidence intervals reflect the uncertainty related to a single number, the parametric mean effect size. Prediction intervals reflect the probable parametric effect size in any study of the same class as those included in a meta-analysis. Its interpretation and applications are different. In this article we explain their different nature and how they can be used to answer specific questions. Numerical examples are included, as well as their computation with the me

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44

Beran, Rudolf, and Peter Hall. "Interpolated Nonparametric Prediction Intervals and Confidence Intervals." Journal of the Royal Statistical Society: Series B (Methodological) 55, no. 3 (1993): 643–52. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01929.x.

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45

Foster, Colin. "Confidence Trick: The Interpretation of Confidence Intervals." Canadian Journal of Science, Mathematics and Technology Education 14, no. 1 (2014): 23–34. http://dx.doi.org/10.1080/14926156.2014.874615.

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46

James, Trevor. "Can we have confidence in confidence intervals?" Assessment and Development Matters 10, no. 3 (2018): 15–19. http://dx.doi.org/10.53841/bpsadm.2018.10.3.15.

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47

Cousineau, Denis. "Varieties of Confidence Intervals." Advances in Cognitive Psychology 13, no. 2 (2017): 140–55. http://dx.doi.org/10.5709/acp-0214-z.

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48

Casella, George. "Refining binomial confidence intervals." Canadian Journal of Statistics 14, no. 2 (1986): 113–29. http://dx.doi.org/10.2307/3314658.

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49

Sherman, Gary J. "Confidence Intervals from Groups." Mathematics Magazine 65, no. 2 (1992): 118. http://dx.doi.org/10.2307/2690492.

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50

Weerahaudi, Samaradasa. "Correction: Generalized Confidence Intervals." Journal of the American Statistical Association 89, no. 426 (1994): 726. http://dx.doi.org/10.2307/2290895.

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