This was clearly written by someone who works in a field where you have to make unsubstantiated assumptions on a regular basis... and likely by someone who hasn't had the frequent experience of making extremely well-informed hypotheses about a biological or clinical outcome only to be roundly disproven after the data is in due to the stunning complexity of biology. I can't tell you the number of times I have been surprised by an outcome, only to learn fundamentally important things that changed my model. I literally just got some data on Thursday that was shocking to me based on a series of previous experiments that had suggested an alternative likely result, and because I tested it (rather than relying on intuition and logic and extrapolating from previous results) I am now redesigning a portion of my technology's process to enhance its safety profile.
People are absolutely right to say they have no data if there is in fact no data. There may be inferences and assumptions we can make based on our experiences, and that informs our hypotheses and trial designs. Science is not performed in a vacuum. But we have the recognize that the level of certainty we have about an outcome is fairly low before we have tested it, especially in a poorly controlled real world context like a clinical intervention. I recognize that in times of great urgency there is value in acting as soon as possible even if all of the data is not yet in - that was the argument much of this forum made when I expressed skepticism about the likely efficacy of widespread cloth facemask usage by members of the public. Yet as the magnitude of the intervention and the attendant risks increases, the reason for caution also increases.





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