 is used when the reading of
a witness cannot be confidently determined for the variation unit in question.
 Taking the UBS4 apparatus of Hebrews as an example, a variation unit with three readings is found at Heb 1:3:
τῆς δυνάμεως αὐτοῦ, καθαρισμόν (01 A B H* P 044 075 0150 33 81 436 1175 1962 2464 itt, v vg syrpal arm geo1 Didymusdub Cyril John-Damascusmss)
τῆς δυνάμεως, δι᾽ ἑαυτοῦ καθαρισμόν (P46 0243 6 424c 1739 1881*)
τῆς δυνάμεως αὐτοῦ, δι᾽ ἑαυτοῦ (D Hc K L 104 256 263 365 424* 459 1241 1319 1573 1852 1881c 1912 2127 2200 Byz Lect itar, b, comp, d vgms syr(p), hcopsa, bo, fay (eth) geo2 slav Chrysostom John-Damascus Augustine Varimadum)
 Encoding these readings as numerals produces a single column of the data matrix. Including all 44 variation units in the UBS4 apparatus of Hebrews produces a data matrix with 154 rows and 44 columns. A separate row is devoted to each scribe and corrector of a manuscript. Some sigla are reformatted to facilitate the eventual plotting of analysis results. In particular, superscripts are replaced with inline forms, and Gregory-Aland numbers are employed for majuscules not represented by Roman letters. Thus, for example, P46 becomes P46, Codex Sinaiticus is represented by the Gregory-Aland number 01, Codex Vaticanus is represented by the letter B, and syrpal becomes syr-pal. This article employs these reformatted sigla from this point forwards.
 Analysis can begin once the data matrix is in hand. Some analytical methods operate directly on the data matrix while others begin with a dissimilarity matrix constructed by calculating the distance between each pair of witnesses.
 Dissimilarity is a measure of how unlike two objects are with respect to each other. When calculating the dissimilarity of two objects contained in a data matrix, it is important to compare them in a meaningful way. Data which encodes the state of an object with a numeral is called “nominal” because each numeral simply nominates the state. Even though the state is represented by a numeral, it does not make sense to compare the encoded states on any basis except equivalence. (In the present case, objects are witnesses and states are readings.) Comparisons on the basis of order, magnitude, or ratio are usually meaningless for nominal data.
 A measure called the “simple matching distance” is suitable for quantifying the dissimilarity between two witnesses whose readings are encoded as nominal data. This distance is calculated by operating on the data matrix as follows:
Extract the rows corresponding to the two witnesses being compared.
Extract the columns where both witnesses are defined (i.e. neither has
Count the number of extracted columns where the readings disagree.
Divide this number of disagreements by the number of extracted columns to obtain the distance.
 As an example, in the rows of the Hebrews data matrix devoted to witnesses P46 and B, there are 23 columns in which the readings of both are defined. Of these, three have different readings. The simple matching distance between P46 and B based on this sample of variation units is therefore 3/23, or 0.130.
 The significance of a distance between two witnesses is affected by certain statistical considerations. Firstly, a range of distances can be expected to occur if the readings of two witnesses are randomly selected from the available pool. Only distances outside this range are significant in a statistical sense. Secondly, the distance between a pair of witnesses calculated from a sample of variation units is only an estimate of the actual distance that would be obtained if every variation unit was counted. A statistical analysis gives a range of values that is likely to include the actual value. Finally, the relative size of the range of values increases as the sample size decreases. (In the present case, the sample size is the number of variation units.) For this reason, the utility of a distance estimate decreases as the the sample size becomes smaller.
 A range of numbers of disagreements can be expected to occur between two witnesses that have no genetic