![]() Dual N- Back FAQ - Gwern. Statistical background: Against null- hypothesis significance testing. Mainstream science is flawed: seriously mistaken statistics combined with poor incentives has led to masses of misleading research. Not that this problem is exclusive to psychology. Medical science in general is often on very shaky ground. The basic nature of significance being usually defined as p< 0. ![]() ![]() Ioannidis 2. 00. 5 points out that considering the usual p values, the underpowered nature of many studies, the rarity of underlying effects, and a little bias, even large randomized trials may wind up with only an 8. One survey of reported p- values in medicine yielding a lower bound of false positives of 1. Yet, there are toomany positive results. US states & vary by period & country - apparently random chance is kind to scientists who must publish a lot and recently!); then there come the inadvertent errors which might cause retraction, which is rare, but the true retraction rate may be 0. How many scientific papers should be retracted?), is increasing & seems to positively correlate with journal quality (modulo the confounding factor that famous papers/journals get more scrutiny), not that anyone paysany attention to such things; then there are basic statistical errors in > 1. N-back is a kind of mental training intended to expand your working memory (WM), and hopefully your intelligence (IQ 1). The theory originally went that novel 2. Love makes people do dumb stuff. But there are practical, easy steps we can take to maintain our privacy during romantic relationships, and changing one simple. The brazen behavior really fits well with the “move fast, break things” mantra. Essentially, Uber made Singapore the first Asian city where its service would be. Nature and the British Medical Journal; Incongruence between test statistics and P values in medical papers, García- Berthou 2. And only then can we get into replicating at all. See for example The Atlantic article Lies, Damned Lies, and Medical Science on John P. A. Ioannidiss research showing 4. For details, you can see Ioannidis’s Why Most Published Research Findings Are False. Begley’s failed attempts to replicate 4. Booth’s Begley’s Six Rules; see also the Nature Biotechnology editorial & note that full details have not been published because the researchers of the original studies demanded secrecy from Begley’s team), or Kumar & Nash 2. Health Care Myth Busters: Is There a High Degree of Scientific Certainty in Modern Medicine? We could accurately say, Half of what physicians do is wrong, or Less than 2. Nutritional epidemiology is something of a fish in a barrel; after Ioannidis, is anyone surprised that when Young & Karr 2. RCTs, 0/5. 2 replicated and the RCTs found the opposite of 5? Attempts to use animal models to infer anything about humans suffer from all the methodological problems previously mentioned. Hot fields tend to be new fields, which brings problems of its own, see Large- Scale Assessment of the Effect of Popularity on the Reliability of Research & discussion. Failure to replicate in larger studies seems to be a hallmark of biological/medical research. Ioannidis performs the same trick with biomarkers, finding less than half of the most- cited biomarkers were even statistically significant in the larger studies. SNP- IQ correlations failed to replicate on a larger data.) On the plus side, the parlous state of affairs means that there are some cheap heuristics for detecting unreliable papers - simply asking for data & being refused/ignored correlates strongly with the original paper having errors in its statistics. This epidemic of false positives is apparently deliberately and knowing accepted by epidemiology; Young’s 2. Everything is Dangerous remarks that 8. NIH ran 2. 0 randomized- controlled- trials of claims, and only 1 replicated) and that lack of multiple comparisons (either Bonferroni or Benjamin- Hochberg) is taught: Rothman (1. Vandenbroucke, PLo. S Med (2. 00. 8) agrees (see also Perneger 1. Multiple correction is necessary because its absence does, in fact, result in the overstatement of medical benefit (Godfrey 1. Pocock et al 1. 98. Smith 1. 98. 7). The average effect size for findings confirmed meta- analytically in psychology/education is d=0. IQ studies); when moving from laboratory to non- laboratory settings, meta- analyses replicate findings correlate ~0. Anderson et al 1. Mitchell 2. 01. 2; for exaggeration due to non- blinding or poor randomization, Wood et al 2. Meta- analyses also give us a starting point for understanding how unusual medium or large effects sizes are. Psychology does have many challenges, but practitioners also handicap themselves; an older overview is the entertaining What’s Wrong With Psychology, Anyway?, which mentions the obvious point that statistics & experimental design are flexible enough to reach significance as desired. In an interesting example of how methodological reforms are no panacea in the presence of continued perverse incentives, an earlier methodological improvement in psychology (reporting multiple experiments in a single publication as a check against results not being generalizable) has merely demonstrated the widespread p- value hacking or manipulation or publication bias when one notes that given the low statistical power of each experiment, even if the underlying phenomena were real it would still be wildly improbable that all n experiments in a paper would turn up statistically- significant results, since power is usually extremely low in experiments (eg. Msn Webcam Hack Spy 007 Watch . These problems are pervasive enough that I believe they entirely explain any decline effects.The failures to replicate statistically significant results has led one blogger to caustically remark (see also Parapsychology: the control group for science, Using degrees of freedom to change the past for fun and profit, The Control Group is Out Of Control): Parapsychology, the control group for science, would seem to be a thriving field with statistically significant results aplenty…Parapsychologists are constantly protesting that they are playing by all the standard scientific rules, and yet their results are being ignored - that they are unfairly being held to higher standards than everyone else.I’m willing to believe that. It just means that the standard statistical methods of science are so weak and flawed as to permit a field of study to sustain itself in the complete absence of any subject matter. With two- thirds of medical studies in prestigious journals failing to replicate, getting rid of the entire actual subject matter would shrink the field by only 3. Cosma Shalizi: …Let me draw the moral [about publication bias]. Even if the community of inquiry is both too clueless to make any contact with reality and too honest to nudge borderline findings into significance, so long as they can keep coming up with new phenomena to look for, the mechanism of the file- drawer problem alone will guarantee a steady stream of new results. There is, so far as I know, no Journal of Evidence- Based Haruspicy filled, issue after issue, with methodologically- faultless papers reporting the ability of sheep’s livers to predict the winners of sumo championships, the outcome of speed dates, or real estate trends in selected suburbs of Chicago. But the difficulty can only be that the evidence- based haruspices aren’t trying hard enough, and some friendly rivalry with the plastromancers is called for. It’s true that none of these findings will last forever, but this constant overturning of old ideas by new discoveries is just part of what makes this such a dynamic time in the field of haruspicy. Many scholars will even tell you that their favorite part of being a haruspex is the frequency with which a new sacrifice over- turns everything they thought they knew about reading the future from a sheep’s liver! We are very excited about the renewed interest on the part of policy- makers in the recommendations of the mantic arts…And this is when there is enough information to replicate; open access to any data for a paper is rare (economics: < 1. Journal of Money, Credit and Banking, which required researchers provide the data & software which could replicate their statistical analyses, discovered that < 1. Lessons from the JMCB Archive) . In one cute economics example, replication failed because the dataset had been heavily edited to make participants look better (for more economics- specific critique, see Ioannidis & Doucouliagos 2. Availability of data, often low, decreases with time, and many studies never get published regardless of whether publication is legally mandated.
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