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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

Experimental biologists, their reviewers and their publishers must grasp basic statistics, urges David L. Vaux, or sloppy science will continue to grow.

Comment | | Nature

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

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.

Perspective | Open Access | | 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

The meaning of error bars is often misinterpreted, as is the statistical significance of their overlap.

This Month | | Nature Methods

Quality is often more important than quantity.

This Month | | Nature Methods

For studies with hierarchical noise sources, use a nested analysis of variance approach.

This Month | | Nature Methods

When multiple factors can affect a system, allowing for interaction can increase sensitivity.

This Month | | Nature Methods

When some factors are harder to vary than others, a split plot design can be efficient.

This Month | | Nature Methods

Incorporate new evidence to update prior information.

This Month | | Nature Methods

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

This Month | | Nature Methods