Statistical inference by confidence intervals: issues of interpretation & utilization

This 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.

Sim J, Reid N

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