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An investigation of the false discovery rate and the misinterpretation of p-values

Overview of attention for article published in Royal Society Open Science, November 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#2 of 1,645)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Citations

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189 Dimensions

Readers on

mendeley
5 Mendeley
citeulike
29 CiteULike
Title
An investigation of the false discovery rate and the misinterpretation of p-values
Published in
Royal Society Open Science, November 2014
DOI 10.1098/rsos.140216
Pubmed ID
Authors

David Colquhoun, Colquhoun, David, Colquhoun D, D. Colquhoun

Abstract

If you use p=0.05 to suggest that you have made a discovery, you will be wrong at least 30% of the time. If, as is often the case, experiments are underpowered, you will be wrong most of the time. This conclusion is demonstrated from several points of view. First, tree diagrams which show the close analogy with the screening test problem. Similar conclusions are drawn by repeated simulations of t-tests. These mimic what is done in real life, which makes the results more persuasive. The simulation method is used also to evaluate the extent to which effect sizes are over-estimated, especially in underpowered experiments. A script is supplied to allow the reader to do simulations themselves, with numbers appropriate for their own work. It is concluded that if you wish to keep your false discovery rate below 5%, you need to use a three-sigma rule, or to insist on p≤0.001. And never use the word 'significant'.

Twitter Demographics

The data shown below were collected from the profiles of 1,712 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 2 40%
Student > Master 1 20%
Student > Bachelor 1 20%
Student > Ph. D. Student 1 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 60%
Psychology 1 20%
Social Sciences 1 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 1516. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 November 2018.
All research outputs
#1,140
of 12,145,483 outputs
Outputs from Royal Society Open Science
#2
of 1,645 outputs
Outputs of similar age
#25
of 267,798 outputs
Outputs of similar age from Royal Society Open Science
#1
of 5 outputs
Altmetric has tracked 12,145,483 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,645 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 50.8. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,798 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them