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Do-it-yourself shuffling and the number of runs under randomness

Author

Listed:
  • Nigel Smeeton

    (King's College, London)

  • Nicholas J. Cox

    (University of Durham, UK)

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. Copyright 2003 by StataCorp LP.

Suggested Citation

  • Nigel Smeeton & Nicholas J. Cox, 2003. "Do-it-yourself shuffling and the number of runs under randomness," Stata Journal, StataCorp LP, vol. 3(3), pages 270-277, September.
    • Handle: RePEc:tsj:stataj:v:3:y:2003:i:3:p:270-277
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    Cited by:

    1. Samaniego de la Parra Brenda & Fernández Bujanda León, 2020. "Increasing the Cost of Informal Workers: Evidence from Mexico," Working Papers 2020-19, Banco de México.
    2. Ozier, Owen, 2012. "Perils of simulation : parallel streams and the case of stata's rnormal command," Policy Research Working Paper Series 6278, The World Bank.

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