Common Errors in Statistics (and How to Avoid Them), 4th Edition

CONCLUSIONS

Our greatest fault (apart from those to which our wives have been kind enough to draw our attention) is to save time by relying on the abstract and/or the summary of a paper for our information, rather than wade through the entire article. After all, some reviewer has already gone through it with a fine-tooth comb. Or have they? Most reviewers, though experts in their own disciplines, are seldom as knowledgeable in statistics. It is up to us to do the job, to raise the questions that ought to have been asked before the article was published.

Is an attempt made to extend results beyond the populations that were studied? Are potential biases described?

Were any of the tests and subgroup analyses performed after the data were examined, thereby rendering the associated p-values meaningless? And, again, one must ask, were all potential confounding factors accounted for either by blocking or by treatment as covariates? (See, for example, the discussion of Simpson’s paradox in Chapter 11.)

Be wary of extrapolations, particularly in multifactor analyses. As the small print reads on a stock prospectus, past performance is no guarantee of future success.

Are nonsignificant results taken as proof of lack of effect? Are practical and statistical significance distinguished?

Finally, few journals publish negative findings, so avoid concluding that “most studies show.”

THE COURTS EXAMINE THE SAMPLING UNIVERSE

The U.S. Equal Employment Opportunities Commission (EEOC) alleged that ...