Wildlife managers routinely compute sets of simultaneous confidence intervals to estimate the actual proportion of use of a set of k habitat types. Confidence intervals are determined by assuming that the counts of observed use are from k binomial populations. A set of k intervals is constructed from a large sample approximation for a confidence interval for a single binomial proportion. The simultaneous confidence level is controlled by use of the Bonferroni inequality. The coverage probability of these intervals can be less than the nominal (1 - a) 100% level. This paper presents results of a simulation study comparing the performance of these intervals with 3 alternatives; the usual method with a continuity correction factor, and 2 methods of computing confidence intervals for multinomial proportions. The 2 latter methods are superior and should be used in place of the binomial intervals. J. WILDL. MANAGE. 60(3):653-658
[1]
G. Casella,et al.
Statistical Inference
,
2003,
Encyclopedia of Social Network Analysis and Mining.
[2]
P. Krausman,et al.
Clarification of a technique for analysis of utilization-availability data
,
1984
.
[3]
Clyde W. Neu,et al.
A TECHNIQUE FOR ANALYSIS OF UTILIZATION- AVAILABILITY DATA'
,
1974
.
[4]
D. C. Hurst,et al.
Large Sample Simultaneous Confidence Intervals for Multinomial Proportions
,
1964
.
[5]
L. A. Goodman.
On Simultaneous Confidence Intervals for Multinomial Proportions
,
1965
.
[6]
C. Blyth.
Approximate Binomial Confidence Limits
,
1986
.