Do–it–yourself shuffling and the number of runs under randomness
Abstract. A common class of problem in statistical science is estimating, as a
benchmark, the probability of some event under randomness. For example, in a
sequence of events in which several outcomes are possible and the length of
the sequence and number of outcomes of each type known, the number of runs
gives an indication of whether the outcomes are random, clustered, or
alternating. This note explains and illustrates a simple method of random
shuffling that is often useful. We show how the conditional probability
distribution of the number of runs may be derived easily in Stata, thus
yielding p-values for testing the null hypothesis that the type of
outcome is random. We also compare our direct approach with that using the
simulate command.
View all articles by these authors:
Nigel Smeeton, Nicholas J. Cox
View all articles with these keywords:
alternation, categorical data, clustering, conditional distribution, forvalues, p-value, permutation, run, sequence, simulate, simulation
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