The correct use of statistics is not just good for science — it is essential.
Statistics for Biologists
There is no disputing the importance of statistical analysis in biological research, but too often it is considered only after an experiment is completed, when it may be too late.
This collection highlights important statistical issues that biologists should be aware of and provides practical advice to help them improve the rigor of their work.
Nature Methods' Points of Significance column on statistics explains many key statistical and experimental design concepts. Other resources include an online plotting tool and links to statistics guides from other publishers.
Image Credit: Erin DeWalt
Experimental biologists, their reviewers and their publishers must grasp basic statistics, urges David L. Vaux, or sloppy science will continue to grow.
P values, the 'gold standard' of statistical validity, are not as reliable as many scientists assume.
One-quarter of studies that meet commonly used statistical cutoff may be false.
The reliability and reproducibility of science are under scrutiny. However, a major cause of this lack of repeatability is not being considered: the wide sample-to-sample variability in the P value. We explain why P is fickle to discourage the ill-informed practice of interpreting analyses based predominantly on this statistic.
As the data deluge swells, statisticians are evolving from contributors to collaborators. Sallie Ann Keller urges funders, universities and associations to encourage this shift.
Animal studies have contributed immensely to our understanding of diseases and assist the development of new therapies, but inadequate experimental reporting can sometimes render such studies difficult to reproduce and to translate into the clinic. This year, a US National Institute of Neurological Disorders and Stroke workshop addressed this issue, and its conclusions are discussed in a Perspective piece in this issue of Nature. The main workshop recommendation is that at a minimum, studies should report on randomization, blinding, sample-size estimation and how the data were handled.