Bayesian Analysis in the Absence of Prior Information? I have always wondered - how confident do researchers tend to be in their "prior" information when deciding to create statistical models using a Bayesian Approach vs. a Frequentist Approach?
As an example : Suppose a team of researchers has some data relating to the healthcare industry - let's say that they are interested in fitting regression models to this data. As such, they are familiar with the scientific background of the data they are working with, but they are unsure what kinds of priors they can place on the parameters of these regression models.
I saw this post over here (What is an "uninformative prior"? Can we ever have one with truly no information?) in which the idea of an "Uninformative Prior" is provided - but apart from the idea of the Uninformative Prior, I can not seem to find more information on this topic.
As such, is the following idea true? When we don't have a lot of confidence on information that can be used to construct Bayesian Priors - is it generally better to stick to Frequentist Approaches instead of "guessing" which Priors will better fit the data and the choice of model?

If you have no information, you can use “uninformative” priors.  Those priors aim to bring as little information as possible, but as you already learned from the What is an "uninformative prior"? Can we ever have one with truly no information? thread, the name is a little bit misleading because even such priors bring some information to the model. That is why more modern recommendation would be to pick a weakly informative prior (centered on something, but very uncertain).
On another hand, using a frequentist model does not mean making no assumptions: you may still assume things like Gaussian likelihood, a linear relationship between the variables, or using regularized regression you are using implicit priors on the parameters, etc. Even if you wanted to make as few assumptions as possible and used a nonparametric model, you would be making some assumptions. For example, say that you would pick $k$NN regression that just averages among "similar" observations. Still, you need to decide on a similarity metric to define what "similar" means and you need to somehow pick the hyperparameter $k$. With Bayesian models, you make additional assumptions by choosing priors, but other approaches also make assumptions.
But, can you have truly no information about something? Say a meteor hits the planet Earth and brings us a new virus from space. A Bayesian statistician needs to build a model on it. They know nothing about space viruses. Hopefully, they know a lot about viruses from Earth, many scientists also did a lot of educated guesses on what extraterrestrial life forms could be and how they could be similar or different to the life on Earth, etc. The scientist in fact has a lot of prior knowledge and assumptions that they could use to come up with priors.
