English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In a assay published in Nature, Valentin Amrhein (University of Basel) and its colleagues Sander Greenland (University of California) & Blake McShane (Northwestern University), present a series of arguments against the stablished “P value-based statistical significance dichotomania”.1 The authors use some insightful practical examples, such as data related to unintended effects of anti-inflammatory drugs risks, and consider the influence of human cognitive trend to simplistically bucket results into “statistically significant” and “statistically nonsignificant” categories and to consider it definitely different. Importantly, the essay clearly states that the authors are not advocating a ban on P values or statistical measures, but that P values should not be treated categorically or to support dichotomization as statistically significant or not. Similarly, a 2016’ statement of the American Statistical Association2 warns against the misuse of statistical significance and P values, which include as a recommendation “don’t say statistically significant”. On the other hand, John P. A. Ioannidis (Stanford University), argue that “retiring statistical significance would give bias a free pass’ and that ‘irrefutable nonsense would rule’”.3 The author states that dichotomous conclusions can be useful for pinning down discoveries in different fields, but the analysis of effect sizes “can often be better than determining whether an effect exists”. While such discussion is at least provoking and mind challenging, this Editorial goal is to take advantage of the “statistical dogma” questioning to draw some attention to steps that precede the statistical analysis and the generation of a given P value. Indeed, a proper study design can even improve the statistical findings power in a measurable way, but also several aspects of study design and the subsequent data analysis may have a significant (forget P values for a moment), but not statistically quantifiable, impact in the data “significance”. The big mistake would be the overvaluation of statistical analysis methods with the undervaluation of experimental design. In the sequence, some practical examples (derived from our research group data) will be used to illustrate how study design can increase the both “statistical significance” and “biological significance”. Genetic studies are usually based in a classic control approach, were controls and subjects presenting a given condition are compared in regards of the occurrence and frequency of genetic variants. In this context, the P value derived from the unaffected and affected individuals’ comparison is essential to draw any conclusion. In such studies, the number of individual in such groups, but also the frequency of the target genetic variation, the frequency of the studied condition, impact the study power and the determination of the P value, and evidently, the conclusions derived from such data. However, experimental design features, apparently incomputable in the study power determination, can also present a significant impact in the analysis outcome. Thus, in this situation, stratified sampling considering these possible confounding factors could balance the study groups and minimize the effect of external variables on the final data analysis. In the periodontal genetic studies, generally affected individuals (presenting some form of periodontitis) are compared with periodontally healthy subjects.4-7 However, in this context, the possibility to control microbial exposure by oral hygiene methods interfere with the exposure factor, and consequently a periodontally healthy population is comprised by subjects that properly perform oral hygiene methods as a routine. Therefore, irrespective of the putative

journal of applied oral science

To P or not to P, is that the question? Rethinking experimental design and data analysis to improve biological significance beyond the statistical significance