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An assessment of the effectiveness of multiple hypothesis testing for geographical anomaly detection


  • Chris Brunsdon
  • Martin Charlton


The practice of multiple significance testing is reviewed, and an alternative to the frequently used Bonferroni correction is considered. Rather than controlling the family-wise error rate (FWER)—the probability of a false positive in any of the significance tests—this alternative due to Benjamini and Hochberg controls the false discovery rate (FDR). This is the proportion of tests reporting a significant result that are actually ‘false alarms’. The methods (and some variants) are demonstrated on a procedure to detect clusters of full-time unpaid carers based on UK census data, and are also assessed using simulation. Simulation results show that the FDR-based corrections are typically more powerful than FWER-based ones, and also that the degree of conservatism in FWER-based procedures is quite extreme, to the extent that the standard Bonferroni procedure intended to constrain the FWER to be below 0.05 actually has a FWER of around 6times10 -5 . We conclude that in situations where one is scanning for anomalies, the extreme conservatism of FWER-based approaches results in a lack of power, and that FDR-based approaches are more appropriate.

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  • Chris Brunsdon & Martin Charlton, 2011. "An assessment of the effectiveness of multiple hypothesis testing for geographical anomaly detection," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 38(2), pages 216-230, March.
  • Handle: RePEc:pio:envirb:v:38:y:2011:i:2:p:216-230

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