New statistics will not help bad science

November 27, 2018

An article in Clinical Chemistry (1) refers to another article by Ioannidis (2) with a recommendation to change the tradition level of statistical significance for P values from 0.05 to 0.005.

The reasons presented for the proposed change make no sense. Here’s why

The first limitation is that P values are often misinterpreted …

If people misinterpret P values, then training needs to be improved, not changing P values!

The second limitation is that P values are overtrusted, when the P value can be highly influenced by factors such as sample size or selective reporting of data. 

Any introductory statistics textbook provides guidance on how to calculate the proper sample size for an experiment. Once again, this is a training issue. The second part of this reason is more insidious. If selective reporting of data occurs, the experiment is biased and no P value is valid!

The third limitation discussed by Ioannidis is that P values are often misused to draw conclusions about the research.

Another plea for training. And how will changing the level of statistical significance prevent wrong conclusions?

Actually, I prefer using confidence limits instead of P values but they provide no guarantees either. A famous example by Youden showed that for 15 estimates of the solar unit made from 1895 to 1961, each confidence interval did not overlap its predecessor.


  1. Hackenmueller, SA What’s the Value of the P Value? Clin Chem 2018;64:1675.
  2. Ioannidis JPA. The proposal to lower P value thresholds to .005. JAMA 2018;319:1429 –30.