ty of a “success” (i.e. disagreement) is the same for each observation. The last condition is approximately true if the population size is much larger than the sample size.
 These limits can be obtained using the R expression
qbinom(c(0.025, 0.975), 23, 3/23).
 Moore and McCabe, Introduction, 582.
 These were selected from the output of an R script written to estimate relative interval widths for a range of sample sizes. The relative width is obtained by dividing the number of distinct counts in the interval by the total number of possible counts. For example, the limits of the confidence interval for a sample size of fifteen are 4 and 11, representing eight distinct counts (i.e. 4, 5, 6, ..., 9, 10, 11). As there are sixteen possible counts (i.e. 0, 1, 2, ..., 13, 14, 15), the relative width of the interval is 8/16 or 50%.
 The 54 objects are comprised of 53 witnesses and the object representing the UBS text.
The constraint upon the minimum sample size requires that all pairs of witnesses share at
least twelve variation units where the readings of both are defined (i.e. are not marked
as indeterminate using
NA). Witnesses that do not satisfy the constraint are
dropped using the following algorithm:
Eliminate from the data matrix all witnesses defined in less than the required number of variation units.
Construct a table that records the number of shared variation units in which both witnesses are defined for each pair of remaining witnesses.
For each pair with an insufficient number, eliminate from the data matrix that witness with the least number of shared and defined variation units taken across all remaining witnesses.
Repeat the second and third steps until no pairs with an insufficient number remain.
 W. N. Venables and B. D. Ripley, Modern Applied Statistics with S (4th ed.; New York: Springer, 2002) 306.
 Christopher Chatfield and Alexander J. Collins, Introduction to Multivariate Analysis (London: Chapman and Hall, 1980) 212.
 Ibid., 216.
 The “Divisive Coefficient” indicates the degree of clustering in the data.
 Divisive clustering only considers the distances between witnesses and does not take account of genealogical information. The resulting chart is not a genealogical tree as would be produced by phylogenetic analysis. Instead, it is a chart that indicates how the group of all witnesses divides into subgroups when the divisive clustering procedure is applied. Details of this procedure are given in the associated documentation accessible through the R statistical environment. For a good example of a phylogenetic analysis relating to New Testament witnesses, see Matthew Spencer, Klaus Wachtel, and Christopher J. Howe, “The Greek Vorlage of the Syra Harclensis: A Comparative Study on Method in Exploring Textual Genealogy,” TC: A Journal of Textual Criticism 7 (2002) http://purl.org/TC/vol07/SWH2002/. The “coherence-based genealogical method” developed by the INTF also uses genealogical information: see Gerd Mink, “Problems of a Highly Contaminated Tradition: The New Testament: Stemmata of Variants as a Source of a Genealogy for Witnesses,” in Studies in Stemmatology II (ed. P. van Reenan, A. den Hollander, and M. van Mulken; Amsterdam: John Benjamins, 2004) 13-85, for an explanation of this method. As an adjunct, the INTF has developed a Genealogical Queries program which provides access to a database developed in conjunction with their ECM: see Klaus Wachtel and Volker Krüger, “Genealogical Queries,” http://intf.uni-muenster.de/cbgm2/en.html.
 Chatfield and Collins, Introduction to Multivariate Analysis, 213, 220. A dendrogram that does imply a natural partition has long “branches” tipped by compact bunches of “leaves;” that is, within-cluster distances tend to be small when compared with between-cluster distances.
 See Bruce M. Metzger and Bart D. Ehrman, The Text of the New Testament: Its Transmission, Corruption, and Restoration (4th ed.; New York: Oxford University Press, 2005) 306-13, for a convenient summary of these text-types.
 Robert B. Waltz, “Family 1739,” http://www.skypoint.com/members/waltzmn