Just finished my last teaching session of the year, "The twelve days of p-value-mas" (thanks to Goodman 2008; https://t.co/V0AKoKeLdv) https://t.co/CBW2Z8dCKn
A dirty dozen: twelve p-value misconceptions.
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Jun 2015 – Dec 2024Where it's discussed
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What people are saying
Discussions highlight the importance of clarifying what p-values are not, with posts emphasizing the confusion between significance and evidence. Words like 'useful,' 'clarify,' and references to the paper's explanations underscore a shared recognition of its value. Some examples include comments on how the article helps understand that p-values do not directly indicate the probability of correctness, and how the paper serves as a primer for interpreting statistical results properly, reflecting both curiosity and recognition of its practical implications.
Most discussions support the idea that the publication effectively clarifies common misunderstandings about p-values, with many affirming its usefulness for proper interpretation.
The group shows high interest in the topic, with multiple mentions of reading, sharing, and appreciating the paper, indicating engagement with statistical misconceptions.
Posts reference specific sections, summaries, and implications of the article, demonstrating at least some deep engagement with the material.
Participants acknowledge that understanding these misconceptions can significantly influence statistical reasoning and research practices, suggesting a moderate to high perceived impact.
Abstract in brief
- The text discusses common misunderstandings about P values in medical research, emphasizing that they are often misinterpreted as a direct measure of evidence or probability of error, which is incorrect.
- It highlights that P values are not part of a formal system of statistical inference and that misinterpreting them can lead to faulty conclusions and overconfidence in experimental results.
- The article also contrasts P values with Bayes factors, which are considered more reliable for judging evidence because they incorporate prior information and are easier to interpret.
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Most liked on X and BlueskyUn valor de p menor de 0.05 no refleja que algo sea clínicamente significativo, siempre tiene que verse en su contexto y su pregunta de investigación Recomendamos el artículo A Dirty Dozen: Twelve P-Value Misconceptions una breve revisión de estos problemas https://t.co/LkCryy3q3T
No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
@trumanfrancis @vamrhein @Lester_Domes @lakens @learnfromerror @f2harrell @stephensenn @goodmanmetrics @statsepi Thank you for sharing it. I didn't know about this paper. I will read it. IMO, the best explanation about “what is” and “what isn't” the P-value are on these articles: 1. https://t.co/DbMoDJlieN 2. https://t.co/vpt61tDaZT 3. https://t.co/cTCmtGOebz
Great paper on the nonsense that is the p-value. "The effect best supported by the data from a given experiment is always the observed effect, regardless of its significance." https://t.co/uBn76tXHqS
@sammy_suyama "The most important foundational issue to appreciate is that there is no number generated by standard methods that tells us the probability that a given conclusion is right or wrong." (Goodman, 2008, https://t.co/dltzqfhTq0) とちゃんと明記されている文献もありました。
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Understanding Neyman-Pearson Tests and Common Misinterpretations
This video explains the theoretical and philosophical misconceptions surrounding Neyman-Pearson acceptance tests, often misinterpreted as providing definitive evidence, leading to pseudoscientific conclusions. It…
Understanding Fisher's Significance Tests and Common Misinterpretations
This video explains how significance tests are often misinterpreted, particularly within NHST. It clarifies common misunderstandings, helping prevent pseudoscientific conclusions and promoting accurate interpretation of…
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@peder_isager This is probably for after they have some understanding of the topic, but I found it really useful to read about all these misinterpretations! Shows you what p-values are NOT! https://t.co/DxAzZr4BS0.
@goodmanmetrics has explain in details in his paper a A Dirty Dozen: Twelve P-Value Misconceptions published in 2011. Link: https://t.co/BeiESai5YV.
@drjohnm I bet that most MDs (and others) might struggle with understanding and accurately defining p-values and confidence intervals — myself included.
2/2 I am not sure how many of us would properly define "simple" things such as p-value and 95% confidence intervals. Probably not many. 10.1053/j.seminhematol.2008.04.003 What do you mean by "made-up data"?
@TomBoylesID @MythoFobe @ChronicUTIAus @xxChronicutixx @KeatingRachelle @StephenTristram I agree – I have read it and a few others: https://t.co/eKEnykzTap https://t.co/J6K0on3T4i https://t.co/nJSJCyIcLH https://t.co/sYp700KkX0 https://t.co/KgH5rM7Yb8
@Mukum3 @mrophiophagus https://t.co/dW1lGmvZ83 https://t.co/NDSKxwoLE2
@MariannaRosso1 Here’s 12 wrong answers :) https://t.co/7Jwu1TSQEk
@ATabarrok @ariarmstrong Goodman, Steven. "A dirty dozen: twelve p-value misconceptions." Seminars in hematology. Vol. 45. No. 3. WB Saunders, 2008.
Weil Twitter den Link in der Ansicht abschneidet, hier der Titel des Papers: "A Dirty Dozen: Twelve P-Value Misconceptions" von Steven Goodman, Seminars in Hematology
RT @unbiasedscipod: Sources: https://t.co/HOYZKNrDTc Daniel WW, Cross CL. Biostatistics: A Foundation for Analysis in the Health Sciences,…
RT @unbiasedscipod: Sources: https://t.co/HOYZKNrDTc Daniel WW, Cross CL. Biostatistics: A Foundation for Analysis in the Health Sciences,…
Sources: https://t.co/HOYZKNrDTc Daniel WW, Cross CL. Biostatistics: A Foundation for Analysis in the Health Sciences, Tenth Edition. https://t.co/Ktr0yrM1c1 https://t.co/lfKaE7dkMz https://t.co/MBpovRakpr https://t.co/7SmZoatger https://t.co/2FKgE4Wqdb 11/11
The paper “A Dirty Dozen: Twelve P-Value Misconceptions” is a good primer for interpreting the p-value. https://t.co/t1CuYhIB7o https://t.co/jQrr5g4NSh
RT @genkuroki: #統計 Steven Goodman A Dirty Dozen: Twelve P-Value Misconceptions 2008 https://t.co/0Omrjo1k3P これは読む価値のある論文です。しかし、帰無仮説が仮定される…
#統計 Steven Goodman A Dirty Dozen: Twelve P-Value Misconceptions 2008 https://t.co/0Omrjo1k3P これは読む価値のある論文です。しかし、帰無仮説が仮定されるのは現実についてではなく、統計モデルであるという点を避ける基本概念の定義を前提にしており、ASA声明的にはまずいと思います。
RT @Abraham_RMI: @trumanfrancis @vamrhein @Lester_Domes @lakens @learnfromerror @f2harrell @stephensenn @goodmanmetrics @statsepi Thank you…
@trumanfrancis @vamrhein @Lester_Domes @lakens @learnfromerror @f2harrell @stephensenn @goodmanmetrics @statsepi Thank you for sharing it. I didn't know about this paper. I will read it. IMO, the best explanation about “what is” and “what isn't” the P-value are on these articles: 1. https://t.co/DbMoDJlieN 2. https://t.co/vpt61tDaZT 3. https://t.co/cTCmtGOebz
Great paper on the nonsense that is the p-value. "The effect best supported by the data from a given experiment is always the observed effect, regardless of its significance." https://t.co/uBn76tXHqS
Just finished my last teaching session of the year, "The twelve days of p-value-mas" (thanks to Goodman 2008; https://t.co/V0AKoKeLdv) https://t.co/CBW2Z8dCKn
RT @revisionxpares: Un valor de p menor de 0.05 no refleja que algo sea clínicamente significativo, siempre tiene que verse en su contexto…
RT @revisionxpares: Un valor de p menor de 0.05 no refleja que algo sea clínicamente significativo, siempre tiene que verse en su contexto…
Un valor de p menor de 0.05 no refleja que algo sea clínicamente significativo, siempre tiene que verse en su contexto y su pregunta de investigación Recomendamos el artículo A Dirty Dozen: Twelve P-Value Misconceptions una breve revisión de estos problemas https://t.co/LkCryy3q3T
A Dirty Dozen: Twelve P-Value Misconceptions https://t.co/pRTMMuNK90
There is simultaneously too much love and too much hate for p-values. I attempt neutrality, as they are unlikely to be replaced any time soon. I can pledge not to use "statistically significant" though (good reads: https://t.co/piguKK2FlZ; @lakens's https://t.co/wTfeDI8wFm) https://t.co/60cqnXlG6o
"A dirty dozen: twelve p-value misconceptions."読んでる P値にまつわる12個の誤解が紹介されているが、自分も正しくP値を理解できていない現実を突きつけられる、、、 https://t.co/b1GulEDfn5
RT @EstadisticaBase: No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
RT @minoyu: @sammy_suyama "The most important foundational issue to appreciate is that there is no number generated by standard methods tha…
@sammy_suyama "The most important foundational issue to appreciate is that there is no number generated by standard methods that tells us the probability that a given conclusion is right or wrong." (Goodman, 2008, https://t.co/dltzqfhTq0) とちゃんと明記されている文献もありました。
RT @gaalcaras: @scoavoux Il est parfois plus simple de mieux comprendre la p-value en étudiant ce qu'elle n'est pas, voir A Dirty Dozen: tw…
@naked_statist @talyarkoni and as for the summary of fisher, it comes from doi:10.1053/j.seminhematol.2008.04.003
RT @AsadoriQ: A dirty dozen: Twelve p-value misconceptions https://t.co/k57Lulcbpo
A dirty dozen: Twelve p-value misconceptions https://t.co/k57Lulcbpo
A Dirty Dozen: Twelve P-Value Misconceptions.
RT @EstadisticaBase: No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
RT @EstadisticaBase: No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
RT @EstadisticaBase: No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
RT @EstadisticaBase: No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
RT @EstadisticaBase: No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
RT @EstadisticaBase: No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
RT @EstadisticaBase: No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
No, el p-valor no es eso. A dirty dozen: twelve p-value misconceptions: https://t.co/a7hlmrmeHl
@scoavoux Il est parfois plus simple de mieux comprendre la p-value en étudiant ce qu'elle n'est pas, voir A Dirty Dozen: twelve p-value misconceptions (2008) : https://t.co/5Lc7sMjgjR
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