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Statistical Inference by Confidence Intervals: Issues of Interpretation and Utilization

Statistical Inference by Confidence Intervals: Issues of Interpretation and Utilization AbstractThis article examines the role of the confidence interval (CI) in statistical inference and its advantages over conventional hypothesis testing, particularly when data are applied in the context of clinical practice. A CI provides a range of population values with which a sample statistic is consistent at a given level of confidence (usually 95%). Conventional hypothesis testing serves to either reject or retain a null hypothesis. A CI, while also functioning as a hypothesis test, provides additional information on the variability of an observed sample statistic (ie, its precision) and on its probable relationship to the value of this statistic in the population from which the sample was drawn (ie, its accuracy). Thus, the CI focuses attention on the magnitude and the probability of a treatment or other effect. It thereby assists in determining the clinical usefulness and importance of, as well as the statistical significance of, findings. The CI is appropriate for both parametric and nonparametric analyses and for both individual studies and aggregated data in meta-analyses. It is recommended that, when inferential statistical analysis is performed, CIs should accompany point estimates and conventional hypothesis tests wherever possible. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Physical Therapy Oxford University Press

Statistical Inference by Confidence Intervals: Issues of Interpretation and Utilization

Sim, Julius; Reid, Norma
Physical Therapy , Volume 79 (2) – Feb 1, 1999
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References (37)

Publisher
Oxford University Press
Copyright
© 1999 American Physical Therapy Association
ISSN
0031-9023
eISSN
1538-6724
DOI
10.1093/ptj/79.2.186
Publisher site
See Article on Publisher Site

Abstract

AbstractThis article examines the role of the confidence interval (CI) in statistical inference and its advantages over conventional hypothesis testing, particularly when data are applied in the context of clinical practice. A CI provides a range of population values with which a sample statistic is consistent at a given level of confidence (usually 95%). Conventional hypothesis testing serves to either reject or retain a null hypothesis. A CI, while also functioning as a hypothesis test, provides additional information on the variability of an observed sample statistic (ie, its precision) and on its probable relationship to the value of this statistic in the population from which the sample was drawn (ie, its accuracy). Thus, the CI focuses attention on the magnitude and the probability of a treatment or other effect. It thereby assists in determining the clinical usefulness and importance of, as well as the statistical significance of, findings. The CI is appropriate for both parametric and nonparametric analyses and for both individual studies and aggregated data in meta-analyses. It is recommended that, when inferential statistical analysis is performed, CIs should accompany point estimates and conventional hypothesis tests wherever possible.

Journal

Physical Therapy – Oxford University Press

Published: Feb 1, 1999

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