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The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). [2] It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another.
Statistical tests used to compare sets of data have been designed for data sets that are either paired or unpaired, making it important to use the correct test to prevent erroneous results. Tests for paired data include McNemar's test and the paired permutation test. Tests for unpaired data include Pearson's chi-squared test and Fisher's exact ...
Similarity between two sets The Jaccard index formula measures the similarity between two sets based on the number of items that are present in both sets relative to the total number of items. It is commonly used in recommendation systems and social media analysis [ citation needed ] .
A contrast is defined as the sum of each group mean multiplied by a coefficient for each group (i.e., a signed number, c j). [10] In equation form, = ¯ + ¯ + + ¯ ¯, where L is the weighted sum of group means, the c j coefficients represent the assigned weights of the means (these must sum to 0 for orthogonal contrasts), and ¯ j represents the group means. [8]
A character is a minimal unit of text that has semantic value. [9] [10] A character set is a collection of elements used to represent text. [9] [10] For example, the Latin alphabet and Greek alphabet are both character sets. A coded character set is a character set mapped to a set of unique numbers. [10]
Re-ordered text has its background color changed to alternately light blue and yellow. Diff-Text is a web-based software tool that identifies differences between two blocks of plain text. It operates on a closed-source model and offers a donation or pay-what-you-want payment option. [1] [2] To be
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The following table defines the possible outcomes when testing multiple null hypotheses. Suppose we have a number m of null hypotheses, denoted by: H 1, H 2, ..., H m. Using a statistical test, we reject the null hypothesis if the test is declared significant. We do not reject the null hypothesis if the test is non-significant.