ain mathematical conditions are met, a dissimilarity can be called a distance. Other dissimilarity measures exist but not all are suitable for nominal data.
 The original text of Codex Vaticanus (B) is missing from Heb 9:14 to the end, explaining in large part why there are only 23 out of 44 variation units where the readings of both P46 and B are defined. (The reading of P46 is undefined at two of the 44 variation units.) Distances are quoted to three significant figures regardless of whether this level of precision is warranted. Being a ratio of two counts, the simple matching distance is a dimensionless quantity; it is a pure number with no unit.
 These were generated using the R statistical computing
environment. (See The R Project for Statistical Computing at
following statement was executed twice to obtain the random numerals:
 This equation employs factorial notation, where n! = n x (n - 1) x ... x 2 x 1. P(k) is the probability of k out of n disagreements and p is the probability of disagreement, which is the complement of the probability of agreement. That is, P(disagreement) = p and P(agreement) = 1 - p, where “P(x)” stands for “probability of x.”
 Using R, critical limits for a binomial distribution can be
calculated with the expression
qbinom(c(0.025, 0.975), n, p), where n is
the number of trials and p the probability of success. In this case, the
alpha level of 0.05 is split between the two tails,
resulting in cumulative probabilities of 0.025 and 0.975. There are ten trials, each
with a probability of success equal to one half (0.5).
 The usefulness of such an estimate is questionable. The extant evidence may not be representative of all phases of textual development. Furthermore, a multiplicity of similar witnesses, such as those with the Byzantine standard text, can skew the result. Fortunately, the UBS4 apparatus subsumes most Byzantine witnesses under a single group symbol, thereby alleviating this problem.
 These critical limits were calculated using an R script written for the purpose. All of the scripts employed to derive results for this article are available upon request. As it happens, the same critical limits are obtained using the binomial distribution with a probability of disagreement equal to the mean probability of disagreement taken across the 23 variation units. Chapter three of Timothy J. Finney, Analysis of Textual Variation, http://purl.org/tfinney/ATV/, provides a comparison of results obtained by binomial, exact, and Monte Carlo calculations.
 Various refinements have been made to the definition of a variation unit. See, for example, those by Ernest C. Colwell and Ernest W. Tune, “Method in Classifying and Evaluating Variant Readings” in Studies in Methodology in Textual Criticism of the New Testament (New Testament Tools and Studies 9; Leiden: Brill, 1969) 98-9; Eldon J. Epp, “Toward the Clarification of the Term 'Textual Variant'” in Studies in the Theory and Method of New Testament Textual Criticism (ed. Eldon J. Epp and Gordon D. Fee; Studies and Documents 45; Grand Rapids: Eerdmans) 60-1; and Gordon D. Fee, “On the Types, Classification, and Presentation of Textual Variation” in Studies in the Theory and Method, 62-6.
 Aland, Barbara, Kurt Aland†, Gerd Mink, Holger Strutwolf, and Klaus Wachtel, eds, Novum Testamentum Graecum Editio Critica Maior: IV: Catholic Letters (Stuttgart: Deutsche Bibelgesellschaft, 1997-). Dr Klaus Wachtel of the INTF kindly supplied the number of variation units.
 There is no guarantee that the interval will contain the actual distance for a given sample: the actual value is either inside or outside the calculated confidence interval. One cannot be sure that a confidence interval contains the actual value unless the entire population is examined.
 See David S. Moore and George P. McCabe, Introduction to the Practice of Statistics (2nd ed.; New York: Freeman, 1993) 372-4. There are four conditions: (1) a fixed number of observations; (2) the observations are independent; (3) there are only two possible outcomes for each observation; and (4) the probabili