"Statistics is a language constructed to assist this process, with probability as its grammar." They continued, "While rudimentary conversations are possible without good command of the language (and are conducted routinely), principled statistical analysis is critical in grappling with many subtle phenomena to ensure that nothing serious will be lost in translation and to increase the likelihood that your research findings will stand the test of time." The rules, which were made available online June 9, have received an extraordinary amount of attention so far with more than 37,000 page views, already making it one of the top 20 most viewed papers in the series, which includes about 60 total papers.. "Straightforward and understandable guidelines as articulated by Kass and colleagues will help tremendously in reminding both students and faculty as to the importance of statistically well-grounded research.
Their paper is an instant 'must-read' for anyone who cares about good and reproducible science." A summary of the 10 rules:#1 -- Statistical Methods Should Enable Data to Answer Scientific Questions Collaborating with statisticians is often most helpful early in an investigation because inexperienced users of statistics often focus on which technique to use to analyze data, rather than considering all of the ways the data may answer the underlying scientific question.
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.
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The scientific results that stand the test of time are those that get confirmed across a variety of different, but closely related, situations.
In many contexts, complete replication is very difficult or impossible, as in large-scale experiments such as multi-center clinical trials.
#2 -- Signals Always Come With Noise Variability comes in many forms, but it is crucial to understand when it is good and when it is noise in order to express uncertainty.
It also helps to identify likely sources of systematic error.