http://www.keepitsimple.team/ Vastauksena käyttäjille @vijarvinen, @Voltan80 ja 5 muulle
Odota, etsin käsitteet sanakirjasta ja palaan asiaan huomenissa. ;)
#muuten, sain ystävältä pääsiäistervehdyksen: "lisää nauloja ristiin".
”Suomalaiset saavat tottua säästökuureihin”
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Asiantuntijan mukaan EU-puolustuksen vahvistaminen vaatii merkittäviä
kustannuksia.
Artikkeli ”Suomalaiset saavat tottua säästökuureihin” julkaistiin
en...
53 minuuttia sitten
5 kommenttia:
todella ongelmallista, että nyt Atria ikään kuin ulkoistaa tuotantoeläinten hyvinvoinnin havainnoinnin asiantuntijoilta maallikoille, joilla ei välttämättä – tai todennäköisesti – ole minkäänlaista kompetenssia arvioida
https://neuroskeptic.blogspot.fi/
http://blogs.plos.org/scicomm/2016/03/21/pseudonyms-in-science-neuroskeptic-speaks-to-neurocritic-dr-primestein-and-neurobonkers/ http://blogs.plos.org/neuro/2014/12/22/how-psychology-and-neuroscience-get-sex-and-gender-wrong-neuroskeptic-goes-in-depth-with-cordelia-fine/ https://pennmindsthegap.wordpress.com/tag/neuroskeptic/
http://www.bristol.ac.uk/expsych/research/brain/targ/news/2017/neuroskeptic-blog-on-two-recent-targ-papers.html act
Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.
The main idea of this study is the same as in our previous one (14). We analyze resting-state fMRI data with a putative task design, generating results that should control the familywise error (FWE), the chance of one or more false positives, and empirically measure the FWE as the proportion of analyses that give rise to any significant results. Here, we consider both two-sample and one-sample designs. Because two groups of subjects are randomly drawn from a large group of healthy controls, the null hypothesis of no group difference in brain activation should be true. Moreover, because the resting-state fMRI data should contain no consistent shifts in blood oxygen level-dependent (BOLD) activity, for a single group of subjects the null hypothesis of mean zero activation should also be true. We evaluate FWE control for both voxelwise inference, where significance is individually assessed at each voxel, and clusterwise inference (19⇓–21), where significance is assessed on clusters formed with an arbitrary threshold.
In brief, we find that all three packages have conservative voxelwise inference and invalid clusterwise inference, for both one- and two-sample t tests. Alarmingly, the parametric methods can give a very high degree of false positives (up to 70%, compared with the nominal 5%) for clusterwise inference. By comparison, the nonparametric permutation test (22⇓⇓–25) is found to produce nominal results for both voxelwise and clusterwise inference for two-sample t tests, and nearly nominal results for one-sample t tests. We explore why the methods fail to appropriately control the false-positive risk.
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