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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

Statistics in biology

The correct use of statistics is not just good for science — it is essential.

Editorial | | Nature News

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.

Commentary | | Nature Methods

As the data deluge swells, statisticians are evolving from contributors to collaborators. Sallie Ann Keller urges funders, universities and associations to encourage this shift.

Comment | | Nature

Practical guides

Low-powered studies lead to overestimates of effect size and low reproducibility of results. In this Analysis article, Munafò and colleagues show that the average statistical power of studies in the neurosciences is very low, discuss ethical implications of low-powered studies and provide recommendations to improve research practices.

Analysis | | Nature Reviews Neuroscience

A protocol providing guidelines on the organizational aspects of genome-wide association meta-analyses and to implement quality control at the study file level, the meta-level across studies, and the meta-analysis output level.

Protocol | | Nature Protocols

This perspective illustrates some of the problems involved in analyzing the complex data yielded by systems neuroscience techniques, such as brain imaging and electrophysiology. Specifically, when test statistics are not independent of the selection criteria, common analyses can produce spurious results. The authors suggest ways to avoid such errors.

Perspective | | Nature Neuroscience

The authors examine papers in high profile journals and find that while collection of multiple observations from a single research object is common practice, such nested data are often analyzed using inappropriate statistical techniques. The authors show that this results in increased Type I error rates, and propose multilevel modelling to address this issue.

Perspective | | Nature Neuroscience

Statistical models called hidden Markov models are a recurring theme in computational biology. What are hidden Markov models, and why are they so useful for so many different problems?

Primer | | Nature Biotechnology