An Assessment of the Effectiveness of Multiple Hypothesis Testing for Geographical Anomaly Detection

Chris Brunsdon and Martin Charlton
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Chris Brunsdon: Department of Geography, University of Leicester, Leicester LE1 7RH, England
Martin Charlton: National Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland

Environment and Planning B, 2011, vol. 38, issue 2, 216-230

Abstract: 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 6 × 10 −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.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:38:y:2011:i:2:p:216-230

DOI: 10.1068/b36093

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