Under growing pressure to report accurate findings as they interpret increasingly larger amounts of data, researchers are finding it more important than ever to follow sound statistical practices.
For that reason, a team of statisticians including Carnegie Mellon University’s Robert E. Kass wrote “Ten Simple Rules for Effective Statistical Practice.” Published in PLOS Computational Biology for the journal’s popular “Ten Simple Rules” series, the guidelines are designed to help the research community — particularly scientists who aren’t statistical experts or without a dedicated statistician as part of their team — understand how to avoid the pitfalls of well-intended, but inaccurate statistical reasoning.
Here are the 10 rules:
#1 – Statistical Methods Should Enable Data to Answer Scientific Questions
#2 – Signals Always Come With Noise
#3 – Plan Ahead, Really Ahead
#4 – Worry About Data Quality
#5 – Statistical Analysis Is More Than a Set of Computations
#6 – Keep it Simple
#7 – Provide Assessments of Variability
#8 – Check Your Assumptions
#9 – When Possible, Replicate!
#10 – Make Your Analysis Reproducible
To read the 10 rules, click here.
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