Statistical analysis of choices

The method of statistical analysis in many bioassays focuses on analyzing the number and pattern of choices made by subjects. In general, these assays will not involve truly continuous variables, but will involve counts, e.g., the number of times that each branch of an olfactometer was chosen, the number of times that upwind flight was observed, the number of eggs deposited on test or control substrates, or the number of times that test or control feeding substrates were selected. Such data often are distributed following a Poisson distribution and can 

If the two choices are not independent (i.e., subjects must accept one of two choices and reject the other), then data should be analyzed as frequencies. For simple choice tests, the G-test or Fisher s exact test (Sokal Rohlf 1995) are often used to test for deviations of the observed pattern of choices from a random pattern. The choice of test depends on sample size and calculated expected values. Sometimes, the proportion of subjects on the test substrate is calculated as T T + C), where T is the number of subjects on the test treatment and C is the number of subjects on the control treatment. These proportions often follow a binomial distribution and can be analyzed as continuous variables after employing the arcsine square root transformation. However, analysis of frequencies is usually preferred to analysis of proportions (Sokal Rohlf 1995). 

Another statistic often calculated is an overall index of acceptability of the form (T- Q/(r + C), where T and C are defined as above. This statistic has an expected value of 0 when there is no difference in preference of the test and control treatments and a range from - 1 (when the test treatment is never chosen) to + 1 where the test treatment is completely preferred over the control. While this statistic allows the expression of the relative attraction and deterrence of a range of compounds, its statistical properties are not well understood. The statistic is not normally distributed, and the occurrence of negative values precludes the use of common transformations (e.g., log, arcsine, square root) to remove some deviations from normality. Thus, if this statistic is used, it should be analyzed only by nonparametric, distribution-free statistical tests based on ranks. Further discussion of procedures and criteria for selecting statistical tests can be found in many standard texts and manuals for a number of statistic analysis packages for use with computers (e.g.. Steel Torrie 1980 SAS Institute 1989 Sokal Rohlf 1995). 

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