I am in great need of help in order to infer the statistical hypothesis tests performed in an old paper. However I need to make some reasonable guesses only from the abstract of the paper since the original text is in chinese which i can not understand (after extensive search i was also unable to find the original text even in chinese)

The title of the paper is: “Detection of sister chromatic exchange in workers exposed to coal tar pitch and to coke oven volatiles"

The abstract of a paper (which was published in 1998 is the following):

In order to know the changes of genetic toxicological effects on workers occupationally exposed to polycyclic aromatic hydrocarbons (PAHs), sister chromatic exchange(SCE) was detected by the methods of peripheral lymphocyte culture in 23 workers exposed to coal tar pitch (CTP) and in 19 workers exposed to coke oven volatiles (COV) and 12 normal controls. The results suggested that the SCE in occupational workers was significantly higher than that in controls (11.31 vs 6.37, P < 0.001). The SCE in workers exposed to CTP and to COV was higher than that of control (10.27 and 12.58 vs 6.37) respectively. In workers exposed to CTP and COV, there were no differences of SCE for smokers and nonsmokers (P > 0.05). It is indicated that CTP and COV caused strong genetic toxicity and injury to chromosome.

In your opinion how do you think that the above reasarch was organized

For example: a) What types of statistical hypothesis testing was performed by the reasearchers b) What kind of data was collected and used for each statistical hypothesis test c) What methods were employed for the each hypothesis test ?


It's impossible to say for sure, but my guess is that they simply did pairwise t-tests. They looked at the average responses from the groups that interested them and did t-tests.

An option for these data would have been a two-way ANOVA (unbalanced, no doubt) with smoking and exposure group as the factors. There is no evidence from the abstract that the researchers attempted any such a thing. The advantage to such a model would be more degrees of freedom for error and better estimate of error (assuming constant variances!).

In my experience, a lot of researchers don't go further than pair-wise t-tests.


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