I would note there is a difference between untrue and fraudulent. "Afflicted by studies with small sample sizes, tiny effects..." Neither these are inherently

I would note there is a difference between untrue and fraudulent. "Afflicted by studies with small sample sizes, tiny effects..." Neither these are inherently the result of malfeasance or fraud, or even poor statistical practice. Sample size is a compromise between a number of things - statistical power, yes, but also trying to minimize the number of human or animal subjects involved, and to be frank, budget (as I've noted elsewhere, the NIH R01 non-modular budget hasn't changed since the 90's). Before there's a body of work done, statistical power is often speculative. What do we think the effect estimate will be. If we're lucky, maybe we have a mathematical model suggesting at least something. In that arena, it's likely that we may undershoot the needed sample size - though I'll note underpowered findings are null findings, and far less likely to get published (and likely hopeless in say...The Lancet). There are also some questions that we may still want answers to where the sample size is inherently small. There are a finite number of Ebola outbreaks, or veterinary clinics in the U.S. (both real examples). Similarly, tiny effects are hard to estimate, but that doesn't mean they're bad to estimate. Something that increased the risk of death in American citizens by 1% for example, would have a relative risk of 1.01, which is as small as many medical and epidemiology journals are apt to report. Yet this would impact thousands of people. Measuring that may be very hard, and very noisy, producing a number of wrong answers, but it's not self-evidently a bad idea. Especially if we don't know if the effect is tiny ahead of time.