disadvantage of bootstrap (from wiki) In wikipedia about disadvantage of bootstrap it says:

The apparent simplicity may conceal the fact that important
  assumptions  are being made when undertaking the bootstrap analysis
  (e.g. independence  of samples) where these would be more formally
  stated in other approaches.

Could you, please, explain this statement?
 A: *

*It's wiki, read all wiki with a grain of salt. You should raise a flag as being unclear, opinion-based, or needing a citation because all of those are (partly) true. The recent influx of people in statistics who feel that broad statements can be made and parroted without formal proof need to be reigned in (I include myself in that statement).

*The bootstrap does not require that samples are independent. There are special bootstrapping procedures that are more efficient than an unconditional bootstrap

*The article makes the critical fallacy of conflating the procedure of generating bootstrap replicates of a dataset (which has no assumptions whatsoever) and obtaining bootstrap intervals/p-values for a test statistic. The BCa, Quantile, Normal Percentile, and Double Bootstrap methods are just a subset of what's out there, and are all developed to be performed on already-bootstrapped replicates of the study data. Basically, there is no one method for getting CIs and p-values, and the weirdness ends up being more a function of the statistic chosen than it is an attribute of the data themselves. 
A: This may be related to the fact that the bootstrap may sometimes be roughly presented as an "assumption free" procedure that can be used to replace other common e.g. tests when their required assumptions (e.g. normality) are not met. However,  bootstrapping is relevant only in certain situations raising assumptions that also have to be met.
