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Statistical inference by confidence intervals: issues of interpretation & utilization

  [ 11 votes ]   [ Discuss This Article ]
www.ProHealth.com • February 5, 1999


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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