Analysis and interpretation of data

Abstract

General advice Our aim in this chapter is to consider briefly some of the main issues that arise in the statistical analysis and interpretation of behavioural data. This is not a statistics textbook, however, and for an account of statistical methodology readers should refer to one of the many excellent statistics books available. Our general advice is not to become obsessed by statistical techniques, nor too cavalier in their use. Statistical analysis is merely a tool to help answer questions, and should be the servant rather than the master of science. The great physicist Lord Rutherford was perhaps over-stating this point when he wrote: ‘If your experiment needs statistics, you ought to have done a better experiment’; biological systems have much greater variability than is encountered in physics experiments and statistical analysis is often essential for understanding what is going on. Nonetheless, excessively complicated statistics are sometimes used as a poor substitute for clarity of thought or good research design. Statistical analysis, no matter how arcane or exquisite, can never replace real data. Besides being sometimes over -used, statistical techniques are frequently mis used in the behavioural literature. We have already outlined one common error – that of pooling data in the mistaken belief that they are independent of one another (section 3.F). Other familiar pitfalls, covered in this chapter, include the inappropriate use of one-tailed tests (section 9.C.5), the conflation of effect size with statistical significance (9.D), the application of parametric tests to data that violate their underlying assumptions (9.F), the misinterpretation of multivariate statistics (9.1) and the various misuses of correlation coefficients (9.G).