Is it really worth doing Bayesian Analysis if you have no idea about Priors? I have heard that if you use uniform priors in Bayesian Analysis, it is the same as doing Frequentist Analysis. If you are creating statistical models and you really have no idea about the prior distribution of the parameters in your model (e.g. maybe you only know that the parameters have to be greater than 0 since they represent concepts in the real world, e.g. human height) - does it really make sense to use Bayesian based models?
I have heard of concepts such as Non-Informative Priors and Jeffery's Priors - but it still seems to me that you can not "pull something out of nothing". If you really have no idea about the values and distributions for your prior parameters - statistically speaking, it would not seem "responsible" to select anything but Uniform Priors for your parameters - thus making your analysis quite similar (if not identical) to the Frequentist approach.
Does anyone have any comments to add to this?
Thanks

Well, the answer is: it depends.
As @BruceET said, Bayesian models can be used to limit the possible values depending on the distribution you assume; furthermore they tend to be more reliable when evaluating rare events and data with high number of cells with 0 counts.
However, when you have a huge number of data, also frequentis models tend to be equally reliable.
It also depends on the type of analysis you are performing: in network meta analysis, when trials with more than two arms are included, bayesian analysis tend to perform better than frequentist, whereas frequentist analysis tend to perform similarly when only trials with twi arms are included (however, bayesian Network also give you better estimation of probability of being the best treatment calcukated with SUCRA or rankograms).
