We present a simple mathematical technique that we call granularity-related inconsistency of means (GRIM) for verifying the summary statistics of research reports in psychology. This technique evaluates whether the reported means of integer data such as Likert-type scales are consistent with the given sample size and number of items. We tested this technique with a sample of 260 recent empirical articles in leading journals. Of the articles that we could test with the GRIM technique (N ¼ 71), around half (N ¼ 36) appeared to contain at least one inconsistent mean, and more than 20% (N ¼ 16) contained multiple such inconsistencies. We requested the data sets corresponding to 21 of these articles, receiving positive responses in 9 cases. We confirmed the presence of at least one reporting …

the granularity-related inconsistency of means (GRIM) test. It evaluates whether reported averages can be made out of their reported sample sizes. Sample sizes need to be small and the data is of whole numbers (e.g. age or likert scale) and means are reported to two decimals.

Table 1 is a nice Bingo sheet for suspect RCTs.

The study found that patients with Covid-19 treated in hospital who “received ivermectin early reported substantial recovery” and that there was “a substantial improvement and reduction in mortality rate in ivermectin treated groups” by 90%.

But the drug’s promise as a treatment for the virus is in serious doubt after the Elgazzar study was pulled from the Research Square website on Thursday “due to ethical concerns”. Research Square did not outline what those concerns were.

A medical student in London, Jack Lawrence, was among the first to identify serious concerns about the paper, leading to the retraction. He first became aware of the Elgazzar preprint when it was assigned to him by one of his lecturers for an assignment that formed part of his master’s degree …

Here we present a tool — the REAPPRAISED checklist — that aims to help readers, journal editors and anyone else assess whether a paper has flaws that call its integrity into question.