How scientific is the science in ethnopharmacology? Historical perspectives and epistemological problems

This commentary is based on a general concern regarding the low level of self-criticism (-evaluation) in the interpretation of molecular pharmacological data published in ethnopharmacology-related journals. Reports on potentially new lead structures or pharmacological effects of medicinal plant extracts are mushrooming. At the same time, nonsense in bioassays is an increasing phenomenon in herbal medicine research. Only because a dataset is reproducible does not imply that it is meaningful. Currently, there are thousands of claims of pharmacological effects of medicinal plants and natural products. It is argued that claims to knowledge in ethnopharmacology, as in the exact sciences, should be rationally criticized if they have empirical content as it is the case with biochemical and pharmacological analyses. Here the major problem is the misemployment of the concentration-effect paradigm and the overinterpretation of data obtained in vitro. Given the almost exponential increase of scientific papers published it may be the moment to adapt to a falsificationist methodology.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

I introduce the new mgof command to compute distributional tests for discrete (categorical, multinomial) variables. The command supports largesample tests for complex survey designs and exact tests for small samples as well as classic large-sample x2-approximation tests based on Pearson’s X2, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read, 1984, Journal of the Royal Statistical Society, Series B (Methodological) 46: 440–464). The complex survey correction is based on the approach by Rao and Scott (1981, Journal of the American Statistical Association 76: 221–230) and parallels the survey design correction used for independence tests in svy: tabulate. mgof computes the exact tests by using Monte Carlo methods or exhaustive enumeration. mgof also provides an exact one-sample Kolmogorov–Smirnov test for discrete data.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

A new Stata command called -mgof- is introduced. The command is used to compute distributional tests for discrete (categorical, multinomial) variables. Apart from classic large sample $\chi^2$-approximation tests based on Pearson's $X^2$, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read 1984), large sample tests for complex survey designs and exact tests for small samples are supported. The complex survey correction is based on the approach by Rao and Scott (1981) and parallels the survey design correction used for independence tests in -svy:tabulate-. The exact tests are computed using Monte Carlo methods or exhaustive enumeration. An exact Kolmogorov-Smirnov test for discrete data is also provided.