The Conversation: "Goodbye P value: is it time to let go of one of science’s most fundamental measures?"

Andy

Retired committee member
An article from 2015 but might be of interest.
How should scientists interpret their data? Emerging from their labs after days, weeks, months, even years spent measuring and recording, how do researchers draw conclusions about the results of their experiments? Statistical methods are widely used but our recent research in Nature Methods reveals that one of the classic science statistics, the P value, may not be as reliable as we like to think.

Scientists like numbers, because they can be compared with other numbers. And often these comparisons are made with statistical analyses, to formalise the process. The broad idea behind all statistical analyses is that they allow the researcher to make somewhat objective assessments of the results of their experiments.
https://theconversation.com/goodbye...e-of-sciences-most-fundamental-measures-38057
 
As they say, a bad workman always blames his tools.

If the researchers are inappropriately generalising effects based on p values, the problem is the researcher, not the statistical tool.
 
Just read Halsey bio. He uses acceleration loggers to record behaviour and energetics in animals. Are these what we need to record daily data?
 
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