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A, 120(3), 253–290. 13. Gong, B. and Sickles, R. C. (1992). Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data. J. Econometrics, 51, 259–284. 14. Retzlaff-Roberts, D. (1996). Relating discriminant analysis and data envelopment analysis to one another. Comput. and Oper. , 23(4), 311–322. 15. Retzlaff-Roberts, D. and Morey, R. C. (1993). A goal-programming method of stochastic allocative data envelopment analysis. Eur. J. Oper. , 71, 379–397.

And Huang, Z. M. (1989). Cone ratio data envelopment analysis and multi-objective programming. Int. J. , 20(7), 1099–1118. 11. Dyson, R. G. and Thanassoulis, E. (1988). Reducing weight flexibility in data envelopment analysis. J. Oper. Res. , 39(6), 573–576. 12. Farrell, M. J. (1957). The measurement of productive efficiency. J. R. Statist. Soc. A, 120(3), 253–290. 13. Gong, B. and Sickles, R. C. (1992). Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data.

That these two factors may roughly offset each other for highly correlated economic time series∗ is suggested by simulations of data-mining activity reported by Lovell [3]. Table 1 contrasts the actual versus the claimed significance level for a variety of values of k and c using the binomial approximation. Denton [2] has extended this mode of analysis by pointing out that since only papers reporting significant results are likely to survive the refereeing process, articles published in the journals would on average contain the exaggerated claims of significance generated by search even if individual researchers themselves did not data-mine.

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