CITATION.cff

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cff-version: 1.2.0
title: Statistical Inference for Biology
message: >-
  Please cite this lesson using the information in this file
  when you refer to it in publications, and/or if you
  re-use, adapt, or expand on the content in your own
  training material.
type: software
authors:
  - given-names: Susan
    family-names: McClatchy
    email: susan.mcclatchy@jax.org
    affiliation: The Jackson Laboratory
    orcid: 'https://orcid.org/0000-0003-3219-2177'
  - given-names: Georgi
    family-names: Kolishovski
    email: georgi.kolishovski@jax.org
    affiliation: The Jackson Laboratory
  - given-names: Gary
    family-names: Churchill
    email: gary.churchill@jax.org
    affiliation: The Jackson Laboratory
    orcid: 'https://orcid.org/0000-0001-9190-9284'
identifiers:
  - type: doi
    value: 10.5281/zenodo.3687134
    description: Zenodo
repository-code: >-
  https://github.com/carpentries-incubator/statistical-inference-for-biology
url: >-
  https://carpentries-incubator.github.io/statistical-inference-for-biology/
abstract: >-
  More data are better than less data, right? When
  interpreted through sophisticated analytical skills the
  answer could be yes. Absent these skills, analysts can be
  tricked by patterns in “big data” that appear by chance.
  This lesson presents statistical skills and knowledge to
  help data analysts in the life sciences to avoid some of
  the most common pitfalls of big data. Lesson material is
  derived from the HarvardX Biomedical Data Science series,
  part of which is published as the book Data Analysis for
  the Life Sciences (Irizarry & Love, 2016).
keywords:
  - inference
  - biology
  - statistics
license: CC-BY-4.0