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A variety of ways to detect problems in small sample social science surveys has been discussed by a variety of authors. Here, several new approaches for detecting anomalies in large samples are presented and their use illustrated through comparisons of seven retracted or corrected journal articles with a control group of eight articles published since 2000 by a similar group of authors on similar topics; all the articles involved samples from several hundred to many thousands of participants. Given the small sample of articles (k = 15) and low statistical power, only 2/12 of individual anomaly comparisons were not statistically significant, but large effect sizes (d > 0.80) were common for most of the anomaly comparisons. A six-item total anomaly scale featured a Cronbach alpha of 0.92, suggesting that the six anomalies were moderately correlated rather than isolated issues. The total anomaly scale differentiated the two groups of articles, with an effect size of 3.55 (p < 0.001); an anomaly severity scale derived from the same six items, with an alpha of 0.94, yielded an effect size of 3.52 (p < 0.001). Deviations from the predicted distribution of first digits in regression coefficients (Benford’s Law) were associated with anomalies and differences between the two groups of articles; however, the results were mixed in terms of statistical significance, though the effect sizes were large (d ≥ 0.90). The methodology was able to detect unusual anomalies in both retracted and non-retracted articles. In conclusion, the results provide several useful approaches that may be helpful for detecting questionable research practices, especially data or results fabrication, in social science, medical, or other scientific research. Full article