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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

  • Nature News | Editorial

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

  • Nature Methods | Commentary

    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.

    • Lewis G Halsey
    • , Douglas Curran-Everett
    • , Sarah L Vowler
    •  &  Gordon B Drummond
  • Nature | Comment

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

    • Sallie Ann Keller

Practical guides

  • Nature Reviews Neuroscience | Analysis

    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.

    • Katherine S. Button
    • , John P. A. Ioannidis
    • , Claire Mokrysz
    • , Brian A. Nosek
    • , Jonathan Flint
    • , Emma S. J. Robinson
    •  &  Marcus R. Munafò
  • Nature Biotechnology | Primer

    Hierarchical models provide reliable statistical estimates for data sets from high-throughput experiments where measurements vastly outnumber experimental samples.

    • Hongkai Ji
    •  &  X Shirley Liu